The Long Run Effects of R&D Place-based Policies:
Evidence from Russian Science Cities∗
Helena Schweiger†
Alexander Stepanov‡
Paolo Zacchia§
19 May 2017
Abstract
We study the long-run effects of a unique historical place-based policy targeting R&D: the
creation of “Science Cities” in former Soviet Russia. The establishment of Science Cities and
the criteria for selecting their location were largely guided by political and military-strategic
considerations, in addition to economic ones. We compare the current socio-economic
outcomes of Science Cities to those of matched localities that were similar at the time of
establishment. We find that despite massive cuts in government support to R&D following
the dissolution of the USSR, Science Cities still host a more educated population, apply
for more patents, and do better in terms of several proxies of economic development even
though they are not more populous. These findings are consistent with a long-run shift
of the spatial equilibrium due to a combination of barriers to labour mobility and localised
knowledge spillovers. By analysing firm-level data, however, we obtain only mixed evidence
in favour of the existence of spillover effects with a large spatial breadth.
JEL Classification: O33, O38, O14
Keywords: Innovation, Russia, science cities
∗We would like to thank Sergei Guriev, Maria Gorban, Andrei Markevich, and Natalya Volchkova forhelpful discussions as well as the participants at the SITE Academic Conference: 25 years of transition,8th MEIDE conference, 2015 PacDev conference, 17th IEA World Congress, 16th Uddevalla Symposiumand seminars at the EBRD, Higher School of Economics and New Economic School for their commentsand suggestions. Irina Capita, Jan Lukšic and Maria Vasilenko provided excellent research assistance. Theviews expressed in this paper are our own and do not necessarily represent those of the institutions ofaffiliation.
†European Bank for Reconstruction and Development (EBRD). E-mail: [email protected].‡European Bank for Reconstruction and Development (EBRD). E-mail: [email protected].§IMT Lucca Institute for Advanced Studies. E-mail: [email protected].
1 Introduction
The effectiveness of public support to science and R&D is a longstanding issue in the
economics of innovation. Both direct subsidies and indirect incentives to research and
science are usually motivated by the existence of positive externalities (or other types of
market failures) which, in the absence of public intervention, cause underinvestment in
R&D. Some specific innovation policies, like the top-down creation of local R&D clusters,
are characterised by a geographical local dimension. In these contexts it is relevant for
policy to assess the spatial extent of knowledge externalities, which correspond to one
of the three agglomeration forces first identified by Marshall (1890), and that parallel the
learning effect from the more recent classification by Duranton and Puga (2004). In ad-
dition, in these cases the debate about the effectiveness of localised innovation policies
mixes with the one about broader (that is, not innovation-specific) place-based policies.
In particular, it is questioned whether place-based policies have any post-intervention
effect in the long run, in the absence of which their net welfare effect is as likely to be
negative as it is to be positive (Glaeser and Gottlieb, 2008).1
This paper examines the long-run impact of a localised innovation policy: the es-
tablishment of highly specialised Science Cities in the territory of modern Russia during
Soviet times. These cities were middle-sized urban centres created by the Soviet govern-
ment according to a grand strategic plan of technological advancement. They hosted a
high concentration of R&D facilities – often the main economic activity in town – typi-
cally built around a specific technological purpose. Science Cities emerged in the con-
text of technological and military competition of the Cold War; unsurprisingly, most of
them were specialised in military-applicable fields, such as nuclear physics, aerospace,
ballistics and chemistry – although a minority of them were focused in other areas. The
above sectors remain, to this day, those in which Russia maintains a comparative tech-
nological advantage.
1Their argument is based on the interaction between congestion effects and spatial agglomeration ex-ternalities – such as those due to local knowledge spillovers – in a spatial equilibrium model that allows formovement of workers across places. In their theoretical framework, place-based policies that move em-ployment between areas are welfare-improving only if they are effective at shifting economic activity to abetter long-run equilibrium, one in which employment is reallocated in such a way that the increase inself-reinforcing agglomeration forces more than countervails the possibly negative effects from increasedcongestion. Multiple equilibria with such features are however only possible if agglomeration externalitiesfeature non-linearities. This has motivated subsequent empirical research aimed at uncovering agglom-eration effects and their (potential) non-linearities. See also the discussion by Glaeser and Gottlieb (2009)and Kline and Moretti (2014b).
1
While one may question whether the institutional context of Russian Science Cities
is comparable to that of other industrialised countries, this historical experience stands
out with some unique features that motivate its analysis. First, the locations of Science
Cities were typically chosen by the Soviet leadership with criteria that are unusual for a
capitalistic market economy. According to historical research on the topic by Aguirrechu
(2009), since the Soviet government had the power to allocate both physical and human
capital where it deemed necessary, the potential for economic development and local
human capital accumulation was typically not, at the margin, a determinant of a loca-
tion’s choice for the establishment of a Science City. Instead, between any two places
that were geographically close and similar in other respects, the choice usually fell over
the one offering more potential for secrecy and safety from outside, foreign interference
(in the form of R&D espionage). This greatly diminishes concerns for selection biases
due to unobserved determinants of future development, which typically affect studies
about innovative clusters in other countries.
Second, the transition to a market economy that followed the dissolution of the So-
viet Union resulted in a large negative shock for Russian R&D, as direct governmental
expenditure in R&D as a percentage of GDP fell by about 75 per cent. Coupled with the
fact that in those years the Russian private sector was only in its infancy, this resulted
in more than half of the scientists and researchers of post-1991 Russia losing their job.
Unfortunately, adequate information about funding directed to former Science Cities
during the transition is not publicly available, but we argue that as a consequence of
the shock many of them were drastically downsized. In fact, only in the early 2000s was
state support officially resumed for some of the former Science Cities: fourteen in total,
which today bear the name of Naukogrady. Together, these developments indicate that
both the initiation and discontinuation of the Science Cities policy were largely driven
by unique factors, mainly orthogonal to determinants of current demographic and eco-
nomic conditions. In addition, controlling for current governmental support enables
us to evaluate to what extent our results depend on it, instead of being caused by other
factors that relate to persistent effects from the Soviet-era policy.
We estimate the effect of the past establishment of a Science City on the follow-
ing present characteristics of Russian municipalities: innovation (patent output), hu-
man capital (university-graduate or PhD-graduate share of the population) and various
proxies of economic development. In order to give a causal interpretation to our esti-
mates, we construct appropriate control groups by employing matching techniques –
2
propensity-score and Mahalanobis matching. In particular, we match Science Cities to
other localities that, at the time of selection, were similar to them in terms of character-
istics that could affect both their probability of being chosen and their future outcomes.
Our main identifying assumption is that, conditional on these variables, the choice of
a locality was determined at the margin by factors – such as the potential for secrecy
– that would be independent from future, post-transition outcomes. In order to im-
plement this strategy we construct a unique dataset of Russian municipalities (rayons),
which combines both historical and recently observed characteristics.
Furthermore, we complement municipality-level empirical analysis with an addi-
tional set of estimates using firm-level data. We use Russian firm-level data from the
fifth round of the Business Environment and Enterprise Performance Survey (BEEPS V),
sampled from regions with Science Cities locations. Using a variety of specifications,
we evaluate to what extent the distance of a firm from a Science City correlates with
its outcomes in terms of innovation and productivity. BEEPS V is particularly useful in
this regard, as it features an innovation module with detailed information about recent
innovative activities by firms. This analysis is meant to evaluate if, in the modern Rus-
sian economy, the effect of Science Cities continues to spill over on other firms that are
located nearby, and to what economic and geographical extent.
Our results can be summarised as follows. In today’s Russia, Science Cities from the
Soviet era apply for more patents, are more economically developed and host a more
educated population than localities that were comparable to them at the time when the
program started. In line with our expectations (motivated by the specific institutional
context), the estimated treatment effect is lower than the raw sample mean difference
for all variables except patent applications, in whose case no ex-ante bias can be at-
tested. In addition, when we restrict the analysis to localities that have no official sup-
port by the present Russian government, our results remain largely unchanged, with one
exception: for patent outcomes whose point estimates decrease by about 60 per cent.
This indicates that long-run persistence of knowledge capital – possibly coupled with
knowledge spillovers – is likely the most relevant mechanism driving the results. This is
in line with the findings from a related study about the long-run effects of Soviet R&D,
which exploits regional variation of R&D employment at the higher administrative level
of Russian oblasts (Ivanov, 2016). The firm-level analysis, on the other hand, provides no
robust evidence that firms benefit from locating close to Science Cities. While further
research may support stronger conclusions, this is suggestive of the fact that spillover
3
effects due to Science Cities (if they exist at all) are limited in scope.
Our paper contributes to various strands of the literature. First, we add to the set of
studies about the evaluation of place-based policies; for a recent survey of the empirical
research see Neumark and Simpson (2014). The majority of these papers analyse spe-
cific policies enacted in the US (Neumark and Kolko, 2010; Busso et al., 2013; Kline and
Moretti, 2014a) or in the EU (Bronzini and de Blasio, 2006; Criscuolo et al., 2012; Givord
et al., 2013). Among the few that focus, like us, on a non-western country, there is a no-
table contribution by Wang (2013) about Chinese Special Economic Zones (SEZs). The
empirical challenges faced by these studies are typically about constructing appropriate
control groups, and disentangling direct effects from spillovers. Methodologically, our
paper is most directly related to the study by Kline and Moretti (2014a) on the Tennessee
Valley Authority; like in their study, we apply a matching strategy in order to uncover the
long run consequences of our policy of interest. Unlike Kline and Moretti, however, we
find that these are not confined to the sector directly targeted by the policy – that is, R&D
– arguably because of the effect of knowledge spillovers.
Second, and relatedly, we contribute to the more general search of agglomeration
effects – and in particular of the third Marshallian force, localised knowledge spillovers
– in urban and regional economics. This has long been a traditional field of investiga-
tion for economic geographers, with a particular interest in innovation clusters. Follow-
ing seminal contributions by Jaffe (1989), Glaeser et al. (1992), Audretsch and Feldman
(1996) and others, a large literature has developed.2 Recently, the issue has caught the
attention of economists working in more diverse fields. Moretti (2004) shows that in US
cities, the level of education of the workforce affects firm productivity across sectors.
Ellison et al. (2010) simultaneously test all three Marshallian theories by looking at the
co-location of plants across industries. Greenstone et al. (2010) demonstrate the exis-
tence of local productivity spillovers following the opening of a so-called Million Dollar
Plant. In two separate contributions, Bloom et al. (2013) and Lychagin et al. (2016) find
an association between firms’ R&D spending and the productivity of those nearby.3
2We propose two fairly recent surveys: Beaudry and Schiffauerova (2009) focus on the Marshall vs.Jacobs debate around the prevalence of, respectively, within- versus between-industry local knowledgespillovers, while Boschma and Frenken (2011) devote special attention to studies within the evolutionaryeconomic geography research agenda.
3Other, related studies discuss to what extent patent citations can be exploited to recover patterns oflocalised knowledge spillovers. The original contribution having documented this association (Jaffe et al.,1993) has been criticised on methodological grounds in the paper by Thompson and Fox-Kean (2005).Later, Breschi and Lissoni (2009) attempted to bridge the gap between the contending parties of that
4
The specific institutional setting of this paper relates it to other, somewhat diverse
contributions about the consequences of historically massive forms of government in-
tervention on long-run economic and technological development, either in Russia or
elsewhere. Cheremukhin et al. (2017) argue that the “Big Push” industrialisation policy
enacted in the USSR under Stalin did not succeed in shifting Russia onto a faster path
of economic development. In addition, Mikhailova (2012) finds negative welfare effects
from the regional demographic policies enacted by the Soviet Union. However, the pic-
ture looks different in the more specific case of R&D policies. The already cited contribu-
tion by Ivanov (2016) demonstrates how Russian regions with more R&D personnel be-
fore the transition do better today at expanding employment in high-tech sectors. Out-
side Russia, Moretti et al. (2016) show that in OECD countries increases in government-
funded R&D for military purposes have positive net effects on TFP, despite crowding out
private expenditures in R&D.
This paper is organized as follows. Section 2 provides a brief account of the historical
experience of Soviet Science Cities. Section 4 describes the data employed in both the
municipal- and firm-level analysis, along with summary statistics. Section 5 outlines our
empirical methodologies. Sections 6 and 7 discuss the empirical results, respectively
for the municipal-level and the firm-level analyses. Finally, Section 8 summarises the
findings and concludes the paper.
2 Historical and Institutional Background
2.1 History of Science Cities
In the context of the Cold War, geopolitical competition between the USA and the USSR,
the Soviet leadership prioritised allocation of the best resources – including human ones
– to sectors considered vital to the country’s national security. Unsurprisingly, around
two-thirds of all Soviet R&D spending was set for military purposes, and almost all of
the country’s high-technology industry was in sectors directly or indirectly related to de-
fence (Cooper, 2012). Science Cities emerged in this environment. They were 95 middle-
sized urban centers which the Soviet government endowed with a high concentration of
research and development facilities, and – in analogy with many other one-company
debate by showing that the geographical concentration of patent citations is largely due to the spatiallocalisation of co-authorship networks.
5
towns that were typical of the Soviet socialist economy (see Commander et al. (2011)) –
they were devoted to a particular scientific and technical specialisation.4
The former Soviet Union was in a way a pioneer in public investment in science
and in place-based policies that focused on R&D. The model of innovation followed
by the Soviet authorities since the early 1930s was the creation of “special-regime en-
claves intended to promote innovation” (Cooper, 2012).5 Proper Science Cities began to
develop around strategically important (military) research centres from the mid-1930s,
and more of them followed after the Second World War. Table A.1 in Appendix A pro-
vides an overview of their location, the year in which they obtained Science City status
and their priority specialisation areas; this information is based on Aguirrechu (2009),
Lappo and Polyan (2008), NAS (2002) and Russian Wikipedia articles.6 Figure 1 maps
the location of Science Cities: almost all of them were located in populated parts of Rus-
sia, with a high concentration in two specific areas (the Moscow region and the Urals).
As they specialised in industries with high technological intensity, Science Cities
needed access to suitable equipment, machinery, energy supply, intermediaries and
qualified personnel. With the purpose of encouraging skilled individuals to relocate to
Science Cities, the Soviet government strove to make the living conditions in these lo-
calities better than the Soviet standard, by making available to residents a wider choice
of retail goods, more comfortable apartments as well as more abundant cultural oppor-
tunities than elsewhere in the country. Typically, the urban characteristics of planned
Science Cities did not match those of contemporary settlements. In fact, while about
two thirds of Science Cities were established by repurposing existing towns, the rest were
built from scratch, with the specific aim of co-locating scientific research centers, train-
ing institutes and manufacturing facilities (Aguirrechu, 2009).
4The term Science City, Naukograd in Russian, was first introduced in 1991 (Ruchnov and Zaitseva,2011). The former Soviet Union was not a Science Cities pioneer — the first Science City was establishedin 1937 in Peenemünde, Germany (Ruchnov and Zaitseva, 2011) — but it implemented the idea to a muchlarger extent.
5These enclaves first appeared as secret research and development laboratories (so-called Experimen-tal Design Bureaus or more commonly, sharashki) in the Soviet Gulag labour camp system. Scientistsand engineers employed in a sharashka were prisoners picked from various camps and prisons, as-signed to work on scientific and technological problems — a task at which they were quite successful.Famous sharashka inmates included Sergey Korolyov (aircraft and rocket designer); the rocket engine de-signer Valentin Glushko; aircraft designers Andrei Tupolev, Vladimir Petlyakov, Vladimir Myasishchev andLeonid Kerber; the geneticist and radiobiologist Nikolay Timofeev-Ressovsky; the inventor of the straight-flow boiler Leonid Ramzin; and the pioneer of aeronautics and space flight Yuri Kondratyuk.
6A definitive list of Science Cities does not exist, but we are confident that the sources we used are thebest available.
6
Starting in the 1940s, with the need to protect the secrecy of the nuclear weapons
program in the Cold War environment (Cooper, 2012), Science Cities whose main objec-
tive was to develop nuclear weapons, missile technology, aircraft and electronics were
closed in order to maintain security and secrecy.7 This had several implications: non-
residents needed explicit permission to travel to closed cities and were subject to doc-
ument checks at security checkpoints; foreigners were prohibited from entering at all;
relocating to a closed city required a security clearance by the KGB; and dwellers had to
keep their place of residence secret.8 Closed cities that specialised in R&D were often
located in remote areas situated deep in the Urals and Siberia – out of reach of enemy
bombers – and were represented only on classified maps.
Another subset of Science Cities stands out: the so-called academic towns (akadem-
gorodki), which are comparable to U.S. university campuses of large dimension. In fact,
academic towns were semi-isolated neighborhoods of a larger city, endowed with R&D
facilities, housing for R&D staff and their families, as well as basic local infrastructure
such as shops, schools and small hospitals. In all other respects academic towns were
dependent on the larger city they were attached to; in this sense, unlike other Science
Cities, they were not self-sufficient entities. Academic towns appeared after the Second
World War to the east of the Urals, as part of the Soviet program to develop Siberia. The
first and largest academic town was founded in the outskirts of Novosibirsk in 1957.9
2.2 Location of Science Cities
Thanks to the cited historical research by Aguirrechu (2009), however, it is possible to
identify some general factors that drove the choice of locations for specific groups of Sci-
ence Cities. Two general themes emerge. First, the relevant natural, socio-economic and
demographic factors that influenced the choice of a place usually varied by the specific
function of a Science City. Second, at the margin the choice of a location over another
7Examples of closed Science Cities include several ROSATOM (State Atomic Energy Corporation) cities,such as Ozyorsk, Snezhninsk, Zelenogorsk, Protvino and Novouralsk (Table A.1 in Appendix A). All butProtvino are still closed.
8Cities specialising in R&D were only a minority of all Soviet municipalities subject to these restrictions.The majority of closed cities were closed because they hosted strategic military troops or were located nearthe border. We do not consider those in our analysis as they were not Science Cities with a focus on R&D.We include (data permitting) all those Science Cities that were closed because their main objective was todevelop nuclear weapons, missile technology, aircraft and electronics (high-tech sectors).
9Other academic towns were created in Apatity (1954), Irkutsk (1988), Krasnoyarsk (1965) and Tomsk(1972) (Aguirrechu, 2009).
7
usually depended on political, military and security motivations that are arguably unre-
lated with the determinants of economic outcomes in a typical market economy. These
two considerations, on which we expand below, inform this paper’s empirical strategy.
Specifically, our matching strategy rests on the assumption that controlling for certain
relevant factors, Science City status is unrelated to current outcomes.
In terms of socio-economic and demographic characteristics that affected the loca-
tion of Science Cities, the most relevant one that is identified by Aguirrechu is, unsur-
prisingly, the pre-existing level of economic and social development. With some excep-
tions, Science Cities were established in the areas of Russia that were the most industri-
alised, urbanised, and with better educated population; this way, Science Cities could
have easier access to qualified personnel or be able to attract it with minor additional
costs. For this reason, Science Cities are also for the most part located in the western,
warmer part of Russia, within the humid continental climatic region typified by large
seasonal temperature differences. Historically, the socio-economic development differ-
entials between Russian regions strongly correlate with temperature gradients along a
longitudinal axis.10
Other geographical factors differ by Science City type. Those engaging primarily in
basic R&D – such as Dubna and Troitsk – were typically semi-isolated, to be found ei-
ther in outer parts of a region or in the territories between major highways and rail-
roads. Science Cities engaging primarily in applied, production-oriented R&D in civil-
or double-purpose industries (such as electronics or aviation), by contrast, were located
either close to the regional capital or in the proximity to transportation links: with a very
Marshallian motivation, these cities were in more need of easy access to both upstream
suppliers and downstream “buyers” (a term to be interpreted in the context of a social-
ist economy). Heavy industry and nuclear technology needed large amounts of water,
therefore Science Cities specialised in those areas were typically built close to rivers or
lakes. For analogous reasons, Science Cities focused in military shipbuilding – such as
Severodvinsk – had to be located on the coast.
The exact location of a Science City, however, often depended on very idiosyncratic
factors whose main motivation was military, political or strategic. In general, Aguirrechu
underlines the fact that, whenever a Science City had to be set in an urbanised and rela-
tively developed region, the choice between any two similar localities usually fell on the
10In Russia, temperature changes more along the west-east axis, than along the north-south axis; thus,for two localities with the same latitude, the eastern one is typically colder.
8
one with the most potential to maintain secrecy and minimise the threat of spying; he
supports this argument with anecdotal evidence. In this respect, it is not surprising that
many Science Cities were established in the proximity of Moscow, close to the central
government and the headquarters of security agencies such as the KGB. At the extreme,
considerations of this kind overrode all the others. In particular, Science Cities special-
ising in some applied R&D fields such as the production of nuclear and strategic arms
faced a much higher threat of bombing and spying, and were located in regions far from
the borders and in poorly populated municipalities far from the regional centre (with
limited transport links). Examples include Sarov and Snezhinsk.11
Some of these idiosyncratic factors depended on other historical and political cir-
cumstances. Following the evacuation of factories from the European part of the Soviet
Union beyond the Urals during the Second World War, those areas developed rapidly.
On the one hand, this may explain the concentration of many Science Cities in the Urals
area. On the other hand, this was the main motivation for the establishment of academic
towns in Siberian regions with rising industrial and strategic importance but limited sci-
entific capacities, in order to foster their industrial development. There is evidence that
the research in natural sciences conducted in these academic towns was directly linked
to the specific issues faced by Siberia (Aguirrechu, 2009).
2.3 R&D and Science Cities after the Collapse of the Soviet Union
Following the dissolution of the USSR, Russia underwent a profound and for the most
part painful transformation from a planned to a market economy. The withdrawal of the
state from many industries dramatically affected the R&D sector. As shown in Figure 2,
the end of the Soviet Union in fact resulted in a significant drop in R&D funding. From
the 1990 level of about 2 per cent of GDP, which is close to the OECD average, Russian
11These two places provide a particularly indicative example of idiosyncratic factors affecting the loca-tion of Science Cities: sometimes, this was determined by the presence of other Science Cities, or lackthereof. Specifically, Snezhinsk (Chelyabinsk region) was established as a double of Sarov (Nizhny Nov-gorod region) with the main purpose of keeping the industry working even if one of the two places weredestroyed, but also to create inter-City competition. As Sarov is located in a relatively remote location inthe European part of Russia, Snezhinsk had to be placed in a similarly out-of-reach area, but to the Eastof Urals. Officials reportedly considered locations in Novosibirsk, Tomsk and Krasnoyarsk regions, but ul-timately decided on Chelyabinsk and Snezhinsk in particular because of the proximity of another ScienceCity, Ozyorsk, which could supply inputs to Snezhinsk. A similar pattern of interplay between decisionsaffecting different Science Cities was not unique; for example, the four places specialized in the produc-tion of enriched uranium were also located far from each other: respectively in the Sverdlovsk, Tomsk,Krasnoyarsk and Irkutsk regions.
9
gross R&D expenditures (GERD) fell to a mere 0.74 per cent in just two years.12 This is
even more dramatic in face of the fact that the Russian GDP shrank significantly in the
initial years of transition, for a total decline by about 50 per cent between 1990 and 1995
according to official government statistics.
The steep fall in GERD was associated with a gradual decrease in the number of
R&D personnel of similar relative cumulative magnitude, as shown in Figure 3. Whereas
in Soviet times the wages of scientists were 10-20 per cent higher than average, they
dropped to 65 per cent of the average wage as early as 1992, following the withdrawal
of the state from the R&D sector (Saltykov, 1997). Even worse, during the 1990s many
scientists did not even receive their salary or received only a fraction of it over extended
periods (Ganguli, 2014). Unsurprisingly, this stimulated the outflow of scientists to the
business sector: 71 per cent of people who left the R&D sector in those years list higher
salaries as the main reason for changing employment. Low remuneration was not the
only reason for researchers to leave the R&D sector: with the removal of previous restric-
tions to individual mobility, scientists were allowed to migrate. However, even according
to the less conservative estimates, emigration accounted for less than 3 per cent of the
total outflow of staff from the R&D sector,13 and was more driven by ethnic factors than
by differential labour market conditions (Gokhberg, 1997).
It is difficult to identify the exact extent to which this shock affected Science Cities.
Lack of consistent municipal or enterprise-level data, aggravated by the secrecy of many
Science Cities at the beginning of transition, impedes a comprehensive analysis. We ar-
gue, however, that dissolution of the USSR implied a massive downsizing, if not suspen-
sion, of the Soviet Science Cities program. Two facts point to this conclusion. First, most
Science Cities were concentrated in military-related R&D, which in aggregate terms un-
derwent a similar decline as the overall GERD in post-1991 Russia. Specifically, in 1991
12The collapse, which was mainly caused by a decline in government spending, led to a dramatic changein the distribution of Russian GERD by source. Whereas in 1991 the government directly funded 95 percent of total R&D in Russia, by 1995 its share shrank to 60.5 per cent. Other sources became more im-portant, namely the enterprise sector, foreign funding and own funds of R&D institutions. Overall, thisstimulated competition within the sector and forced R&D institutions to expand to such R&D-related ac-tivities as consulting and information services.
13Graham and Dezhina (2008) cite estimates of a number of researchers between 7,000 to 40,000 leavingRussia in the period from 1993 to 1996. Using official statistics, Gokhberg and Nekipelova (2002) estimatethat approximately 20,000 individuals working in the “Science and Scientific Services” sector emigratedduring the 1990s, although many of these individuals may not have been research scientists. Gokhberg(1997) cites a study by CSRS finding that emigrants accounted for only 0.5 per cent of the total outflow ofstaff from the R&D sector. He also estimates the emigrations rates of Russian scientists between 1992 and1999 to be between 0 and 2.4 per cent.
10
defense R&D accounted for 0.75 per cent of Russian GDP, eventually falling to about
0.2 per cent – a figure comparable with the OECD average but half that in the US. Af-
terwards, it has been fluctuating around this level until the early 2000s, when a steady
increase can be identified (see Figure 4). Second, the Russian government has, starting
in the early 2000s, re-established direct support for selected previous Science Cities. In
total, only 14 localities bear the official status of Naukogrady in today’s Russia. This re-
flects the choice to resume the previous system of public R&D support, albeit to a much
smaller scale and in a more concentrated fashion.
3 Theoretical Hypotheses
In light of the historical and institutional background described above, we present a
few hypotheses about the long-run consequences of the Soviet-era Science Cities pro-
gram that we then test statistically. To motivate them, we implicitly refer to a theoretical
framework à la Glaeser and Gottlieb (2008); in that context, non-linearities of urban ag-
glomeration externalities (here, localised knowledge spillovers) would induce a change
in the spatial equilibrium and thus persistent effects even after the discontinuation of
the policy (the past R&D investment).
Municipal-level hypotheses
HM.1 Municipalities with Science Cities have retained a higher stock of knowledge cap-
ital than similar municipalities without Science Cities. While the former enjoyed a
higher concentration of knowledge capital than the latter during the Soviet times,
persistent effects in the modern Russian market economy may be due to either:
(i) a change in the spatial equilibrium due to agglomeration effects; (ii) frictions to
internal mobility and emigration after the collapse of the Soviet Union (resulting
in many researchers staying put), (iii) continued governmental support for local
R&D. The aggregate stock of knowledge/human capital is proxied by the share of
population with a university degree or a PhD.
HM.2 Municipalities with Science Cities are more innovative than similar municipali-
ties without Science Cities. This would be a consequence of HM.1, as cities with
higher knowledge capital are more likely to engage in innovative activities. We
proxy the latter by the number of patents applied for at the foreign patent offices.
11
HM.3 Municipalities with Science Cities are more economically developed than simi-
lar municipalities without Science Cities. This, in turn, would follow from HM.1,
because of aggregate returns to knowledge capital coupled with spillover effects,
as well as from HM.2, because of returns to innovations. We use several proxies
for the level of development: nighttime lights as well as the number, density and
labour productivity of SMEs (in total economy and in manufacturing only).
Firm-level hypothesis
HF.1 Firms located closer to Science Cities are more innovative and better performing
than firms located further away. Because of HM.1, Science Cities may spur innova-
tion and productivity effects in firms located nearby due to knowledge spillovers.
We look at whether firms closer to Science Cities are more likely to engage in any
R&D activity, introduce a new product or process, whether they have a larger share
of their sales due to their innovations, or are more productive.
4 Data and Descriptive Statistics
We evaluate the long-run effects of Science Cities by employing a unique dataset, which
contains information previously unavailable in electronic format. Specifically, it com-
bines: (i) our database on Science Cities, which is described in Section 2 and reported
in Appendix A; (ii) municipal-level data that aggregate various sources about historical
and current characteristics of Russian cities; and (iii) a firm-level database that is ob-
tained by merging BEEPS V Russia and Bureau van Dijk’s Orbis data. In what follows, we
describe the latter two.
4.1 Municipal-level Data
We construct a municipal-level data set for all Russian municipalities at the rayon ad-
ministrative level.14 We obtain administrative data from official sources, and we merge
municipalities to different types of data with the support of GIS software. We manually
assign Science City status to each municipality; as some of these include more than one
14In this paper, we use the English term “municipality” to denote the corresponding Russian rayon ad-ministrative level, and the word “region” to refer to any area at the higher oblast, krai or republic level. InRussia, these three administrative levels do not overlap.
12
historical Science City, however, in total the data include 88 municipalities with at least
one Science City.15 In a few cases historical and current municipal boundaries do not
match exactly, thus we check our data manually. Data types and sources are described
in more detail in Appendix B; here we briefly summarise them distinguishing between
current socio-economic outcomes, historical and geographical characteristics.
Current Outcomes. Our variables of interest on current characteristics of Russian
municipalities match our hypotheses HM1-3 outlined in Section 3. Specifically, we ex-
tract data about the overall municipal population, the share of this population that at-
tained higher education qualifications, and the share of the population holding a doc-
toral degree (Ph.D. equivalent) from the 2010 Russian Census. We proxy innovation by
the number of patents with a local author address that were applied for at the European
Patent Office (EPO) between 2006 and 2015. Finally, as accurate GDP data at the rayon
level is unavailable in Russia, we use several proxies for economic activity: average light
emitted into space at night (night lights) in 2009-2011,16 as well as a number of vari-
ables about small and medium-sized enterprises (SMEs) from the 2010 SME census by
the Russian Statistical Office (ROSSTAT).17 Regarding the latter, we look at the overall
number, the density and the labour productivity of SMEs.
Geographical Characteristics. We collect or calculate municipality-specific infor-
mation about several geographical characteristics: municipal area, average altitude, as
well as average temperatures in January and July. As Science Cities specialised in some
heavy industries needed to be located close to large amounts of water, we also collect
data on each municipality’s access to the coast, to a major river or to a major lake. We
code these both as dummy variables and as distances between the municipality’s geo-
graphical centroid and the closest source of water in question.
Historical Characteristics. Most Science Cities needed access to good transporta-
15NAS (2002) lists four Science Cities for which only their Soviet-era nomenclature is publicly available:Krasnodar-59, Novosibirsk-49, Omsk-5 and Perm-6. Their exact location is still unclear; thus we excludethese four places from the analysis as they cannot be matched to any municipality. In addition, three pairsof Science Cities are located within the same municipalities (Lyubertsi and Tomilino in Lyuberetsky rayon,Avtopoligon and Orevo in Dmitrovsky rayon, and Peresvet and Remmash in Sergiyevo-Posadsky rayon).Hence, 91 Science Cities are mapped to 88 municipalities with at least one Science City.
16Night lights can plausibly be used as a proxy for economic activity under the assumption that lightingis a normal good; see Donaldson and Storeygard (2016). Examples of papers using night lights as a proxyfor economic activity within geographic units for which no alternative data source is available includeHodler and Raschky (2014) and Storeygard (2016).
17According to the official documents, the study was a full-scale federal statistical observation. However,since all operating SMEs were required to participate we consider it as a census.
13
tion links, while others had to be located in remote areas far from espionage threats. To
account for both factors we use GIS data on Russian railroads in 194318 as well as on the
USSR borders; we code them both as dummy variables and as distances from the mu-
nicipal geographic centroid. To control for the existing level of industrial development
in a municipality, we use information about the number of plants (factories, research
and design establishments) of the Soviet defence industry in 1947 from Dexter and Ro-
dionov (2016). Furthermore, to make sure that we match municipalities of similar size
in terms of population, we use data from the first post-World War II census held in the
Soviet Union, conducted in January 1959. We would prefer to use population data from
the 1940s but there was no census conducted until 1959; moreover, World War II affected
the Russian demography so much that any figures collected before 1941 are inadequate.
Since the 1959 census does not have data on population with higher education at the
municipal level, we use data on the number of higher education institutions and R&D
institutes located in a municipality in 1959 as a proxy for it.
Summary Statistics. Table 1 displays summary statistics for the municipal-level
characteristics and outcomes, distinguishing between municipalities with Science Cities
and all other Russian municipalities. It shows that, on average, Science Cities were lo-
cated in more populous and warmer places, with a higher historical concentration of in-
dustrial plants, universities, and R&D institutes. In addition, all our “current” outcome
variables register positive and significant differences. The empirical analysis is meant to
evaluate if these still hold upon comparing Science Cities to localities that were similar
to them at time of their establishment.
4.2 Firm-level Data Sources
To perform our firm-level analysis, we use the fifth round of BEEPS merged with Bureau
van Dijk’s Orbis database – both for Russia only. BEEPS is a firm-level survey conducted
by the European Bank for Reconstruction and Development and the World Bank. It is
based on face-to-face interviews with managers of registered firms with at least five em-
ployees. Stratified random sampling is used to select eligible firms to participate in the
18In the Soviet economy, railroads were the workhorse of the transportation network; road transportplayed only a secondary role (Ambler et al., 1985). Most of the railroads’ construction took place in tsaristRussia; even in Soviet times railroads were not important just for transportation and mobility, but also asdrivers of regional industrialisation. Using information about the railroad network in 1943 is preferable tolater dates, because the Soviet rail transport became one of the most developed in the world after WorldWar II, driven by the country’s need to extract – and transport – its natural resources.
14
survey. Strata are defined by sector (typically manufacturing, retail and other services),
size (5-19, 20-99 and 100+ employees) and regions within a country. The fifth round of
the survey (BEEPS V) was completed in Russia in 2012 with 4,220 top managers (CEOs)
across 37 regions, with at least one in each federal district. The database contains GPS
coordinates of the firm’s location, based on which we can determine distances from Sci-
ence Cities. Additional information about BEEPS V Russia can be found in Appendix C.
Outcomes. BEEPS V included, for the first time, an Inovation Module. This lets us
look at whether in the last three years prior to the survey the firms engaged in R&D,
introduced a new product, process or technological innovation and the share of their
sales due to newly introduced products. We manually clean the information contained
in the innovation module – that is, for each firm we verify whether survey responses
match the firm’s main product and industry, and compare the descriptions of the main
new product or process reported in the survey with the definitions given in the Oslo
Manual (OECD and Statistical Office of the European Communities, 2005), reclassify-
ing those that do not match.19. In addition, we are able to match about 75 per cent of
BEEPS V firms to Orbis accounting data, and thus obtain officially reported measures
of economic performance for a subset of firms (rather than self-reported ones in BEEPS
V). Specifically, we use the log of raw labour productivity as well as the log of operating
revenue; the latter is used as a proxy of total output in a reduced form model akin to a
Cobb-Douglas production function.
Controls. BEEPS V Russia also contains measures for several firm characteristics,
such as: age, industry, exporter status, ownership, geographical scope of the main mar-
ket, exporter status (direct or indirect), the number of permanent, full-time employees,
as well as the share of employees with a university degree. We include them on the right-
hand side of all our empirical models with an innovation-related outcome. For regres-
sions with log labour productivity we omit the employees variable; for regressions of log
operating revenue we include the logs of all “conventional inputs” (number of employ-
ees, total assets and the cost of materials).
Summary Statistics. Table 2 reports descriptive statistics at the firm level. Notice-
ably, more than a fifth of firms (21.6 per cent) reports at least some type innovation in
the last three years prior to the survey; although the fraction of those performing R&D
is much lower (11.1 per cent). Innovative products on average generate 3.9 per cent of
19More details about the cleaning process can be found in Appendix C
15
total sales.
5 Empirical Methodology
5.1 Municipal-level Analysis
In order to test our hypotheses HM.1-3 we compare the long-run outcomes Yi q of mu-
nicipalities with Science Cities with those of municipalities without Science Cities (so-
called “ordinary” municipalities) that are similar to the former in terms of geographical
and historical characteristics Xi k . Here, i = 1, . . . , N stands for municipalities, q = 1, . . . ,Q
indexes different long-run outcomes, and k = 1, . . . ,K different physical and historical
characteristics (covariates).
Our identifying assumption in estimating the long-run impact of Science Cities - the
average treatment effect on the treated (ATT) - is that, conditional on the observed ge-
ographical and historical characteristics, the establishment of Science Cities does not
depend on factors that would affect future outcomes:
Yi q (1) ,Yi q (0)⊥Di∣∣ Xi 1, . . . , Xi K (1)
where Di is equal to one for municipalities with Science Cities and zero otherwise, while
Yi q (1) and Yi q (0) correspond to the long-run outcome Yi q for, respectively, Di = 1 and
Di = 0.
In this setting, the rationale of the Conditional Independence Assumption (1) is pro-
vided by the discussion about the factors affecting selection of Science Cities locations
in section 2.2. In particular, between any two otherwise similar locations, the choice of
the Soviet leadership seemed to be guided more by strategic and military considerations
rather than economic ones, as one would otherwise reasonably expect in the context of
a market economy. Nevertheless, we control for a number of factors that might have af-
fected both the choice of the Soviet government and future outcomes, such as: a munic-
ipality’s population; the number of industrial plants, R&D institutes and universities at
the onset of the Cold War; access to transportation links (distance from railroads); access
to major water sources (distance from the coast, major lakes or major rivers); likelihood
of foreigners’ intrusion (distance from the USSR borders) and climate characteristics
(temperature in January and in July, average altitude in the municipality’s territory).
16
We estimate the ATT via nonparametric matching estimators obtained following two
different procedures. Our main results are obtained via the propensity score matching
(PSM) technique: a method for constructing a control group by associating – in this case
– to each municipality with a Science City a number of ordinary municipalities that are
similar to them in terms of the propensity score p (xi ). The latter is, in turn, an esti-
mate of the probability of hosting a Science City for a municipality with characteristics
xi = (Xi 1, . . . , Xi K ); the usual motivation for PSM is that for any two groups with the same
distribution of p (xi ), the joint distribution of all the covariates must also be the same.
We estimate p (xi ) by employing binary outcome models; we assign to each treated unit
one nearest neighbour (with replacement) and then we ensure that covariates are bal-
anced – that is, that their empirical distribution is similar – between the treated and the
constructed-control groups.
A concern with the PSM technique is that it may associate municipalities with Sci-
ence Cities to ordinary municipalities from more distant regions of Russia, thereby mak-
ing the two groups less comparable. We tackle this issue in two ways. First, we include a
flexible polynomial of municipal coordinates (possibly including interactions between
latitude and longitude) in the specification of the propensity score model. As Science
Cities tend to concentrate in specific areas of Russia, this approach should help con-
struct a control group that is located in the same region or nearby. An alternative is to
employ the Mahalanobis matching technique, by which a treated unit i and a control
observation j are matched if their Mahalanobis Distance:
mi j(xi ,x j
)= (xi −x j
)TΣ
(xi −x j
)(2)
(where Σ is the empirical covariance matrix of x) is small enough. Specifically, we as-
sign one nearest-neighbour control (with replacement) to each municipality with a Sci-
ence City, where treated-control distance is given by mi j . We include municipal coordi-
nates into the vector xi , to ensure that municipalities with Science Cities are matched to
“close” places.
Relative to PSM, however, Mahalanobis matching has its own specific drawbacks: in
particular, it is known to perform worse with a high number of covariates, or when co-
variates are not normally distributed (Gu and Rosenbaum, 1993; Zhao, 2004). In order to
address this, we match on the logs of covariates with highly asymmetric empirical dis-
tributions (average altitude, historical population, number of plants, universities and
17
R&D institutes).20 Moreover, instead of matching on measures of the distance of the
municipality from railroads, rivers and lakes, we force exact matching on corresponding
dummy variables denoting whether any railroad, river or lake was actually present in a
given municipality. We expect that, if all these measures are effective at removing con-
cerns for violations of the Conditional Independence Assumption (1), both propensity
score and Mahalanobis matching should return similar estimates of the ATT for all our
outcomes of interest.
The sample coverage differs across outcome variables. Specifically, SME data are
missing for a few regions and municipalities, including some with Science Cities. For
this reason, we implement different propensity score models for non-SME and SME-
based outcomes. In addition, for both PSM and Mahalanobis matching we evaluate
the quality of post-matching covariate balance separately across the two groups of out-
comes. Still, once we associate the q-th outcome of a matched control Ysq (0) to each
treated unit with Ysq (1), we estimate the ATT for all q = 1, . . . ,Q uniformly as:
�AT T q = 1
S
S∑s=1
(Ysq (1)− Ysq (0)
)(3)
where s = 1, . . . ,S indexes successfully matched pairs. Notice that, as our data cover the
universe of Russian municipalities (with the exception of a minority of places for which
SME data is missing), there is no need to apply sampling weights. We calculate stan-
dard errors following Abadie and Imbens (2006), taking into account that the propensity
score is estimated in the case of PSM. In the case of Mahalanobis matching, we calcu-
late estimates of the ATT by applying a “bias-adjustment” formula (Abadie and Imbens,
2011), which is required when matching is performed on more than one covariate.
5.2 Firm-level Analysis
In order to evaluate whether the effect of Science Cities extends beyond the borders
of a specific municipality, and the breadth of their reach (HF.1) we employ firm-level
data. More specifically, we estimate a number of probit models with the following latent
20Clearly, many municipalities featured no plants, universities or R&D institutes at the time when Sci-ence Cities started to be established. In the computation of Mahalanobis distances, we substitute each ofthese covariates Xi k with the corresponding quantity xi k = log(Xi k +1).
18
variable representation:
I∗f r =α+βS∑
s=1exp
[−Dist(
f , s)]+ L∑
`=1γ`W f r,`+ηr +ε f r (4)
where f = 1, . . . ,F indexes firms; s = 1, . . . ,S denotes Science Cities; r is a subscript for
Russian regions (oblast level); I∗f r is the latent variable associated with one specific in-
novation binary outcome I f r ; Dist(
f , s)
is the geodesic distance between the firm f and
Science City s;(W f r,1, . . . ,W f r,L
)is a set of L additional controls; ηr is a region fixed effect;
and finally ε f r is an error term which is distributed as a standard normal.
The main parameter of interest is β, which measures the relationship between firm
innovation and their geographic proximity to Science Cities. Note that exp[−Dist
(f , s
)]expresses exponential decay of a Science City’s influence in space: it is equal to 1 if a firm
is located right in the centre of a Science City, and it is negligible unless firm f and city s
are relatively close to each other. Thus, if a firm is located in a relatively isolated Science
City, the quantity β · φd – where φ f is the standard normal density function evaluated
at the parameter estimates and at firm f ’s values of the RHS variables – approximates
the marginal effect of a Science City on the probability of a positive realisation of I f r for
firm f . This specification is flexible, and it allows for the effect of Science Cities to be
“cumulative” in areas with a high concentration of Science Cities.
In addition, we estimate via OLS a linear version of model (4) for a few continuous
outcome variables:
P f r = α+ βS∑
s=1exp
[−Dist(
f , s)]+ L∑
`=1γ`W f r,`+ ηr +υ f r (5)
where P f r is some firm-specific performance measure. In this case, β is more easily
interpreted as the average change in P f r for firms that are located in a “relatively iso-
lated” Science City. We allow the set of specified controls(W f r,1, . . . ,W f r,L
)to vary across
models depending on the left-hand side outcome of interest.
While we do not attempt to give any causal interpretation to our firm-level results,
we notice that concerns about endogeneity are limited in this setting. Since the creation
of Science Cities predates the establishment of most modern Russian firms – virtually
all in our sample – the only way for the distance-based regressor and the error term to
be correlated is if a Science City attracts or otherwise encourages the location of more
innovative or better performing firms in their proximities. Still, we make no attempts to
19
correct for this possible instance of endogeneity. Our interest, in fact, is about evaluating
in a descriptive sense whether any relationship between Science Cities and firm-level
outcomes extends in space, and we do not intend to remove a potential mechanism by
which such relationships may manifest themselves.
6 Empirical Results at the Municipal Level
6.1 Propensity Score Matching: All Science Cities
We implement two separate propensity score matching (PSM) algorithms in order to ob-
tain corresponding estimates of the ATT for two different sets of outcomes Yi q . The first
set pertains to all the outcomes that are not based on our SME dataset, specifically “sim-
ple” and “fractional” patent applications, census data (population, graduate share, PhD
share) and the lights emitted into space at night as a proxy of economic development. In
this case, PSM is performed on the entire dataset of Russian municipalities. The second
set relates to the outcomes derived from our SME dataset (number, density and labour
productivity of SMEs; both for total economy and manufacturing only). SME informa-
tion is missing for some regions and cities, including some Science Cities; in order to
ensure that the covariates balance on the restricted SME sample, we perform a separate
matching procedure based on a different p-score estimation model.21
6.1.1 Propensity Score Estimation
Table 3 shows the results of the two p-score estimation models respectively for the non-
SME (model 1) and the SME (model 2) groups of outcomes. Both are probit models, but
their specifications differ: either model includes some squared terms for some covari-
ates Xi k that are not included in the other. Model selection is determined by the quality
of covariate balance resulting from matching, on the predicted p-score, via a nearest-
neighbour algorithm with replacement. Both models are estimated on the whole sam-
ple, but in the case of SME outcomes matching is performed on municipalities – both
“treated” and “control” – for which SME information is available.
It is worth discussing some characteristics of the estimated selected models. Both
have strong predictive power, as indicated by the high pseudo-R2 and highly significant
21We notice that the Science Cities for which SME information is missing are former and current closedcities (see section 2). This may explain why these cities are not included in the 2010 SME census data.
20
model χ2 statistics. The probit coefficients associated with most variables are statisti-
cally significant, with some exceptions. The coefficients of both the linear and squared
term of the “distance from railroad” variable are not statistically significant in either
model; a joint χ2 test of significance, however, returns a p-value of 2.4 per cent in model
(1) and a p-value of 1.5 per cent in model (2). In both models, distances from lakes or
from the USSR border are not statistically significant in joint tests. In model (2), the
temperature-related variables are jointly significant with a p-value of 1.9 per cent, but
the number of universities and the number of industrial plants are not statistically sig-
nificant.
6.1.2 Quality of Matching
We match Science Cities to other municipalities via 1:1 nearest neighbour matching on
the propensity score (with replacement) separately for the two sets of outcomes. Be-
cause they fall outside common support, we are unable to match four Science Cities in
the case of non-SME outcomes and two Science Cities in the case of SME outcomes.
Appendix E provides additional details, in the form of figures, about the common sup-
port for the propensity score as well as kernel density estimates of its empirical distribu-
tion (for both treated and control observations, before and after matching, and for both
groups of outcomes). They show that the balancing property is adequately satisfied in
all cases.
Table 4 displays statistics about covariate balance between treated and control cities
in both matched samples. In all cases, standardised biases are below 10 per cent, t-tests
reject the null hypothesis that any covariate is different between treated and controls,
and also the variance ratios denote that matching achieves a remarkably good balance
in the second moments of the covariates’ distributions. Overall, the treated and con-
trol samples differ by about 15 standard deviations in the case of matching for non-SME
outcomes, and by about 23 standard deviations in the case of matching for SME out-
comes.22
6.1.3 ATT Estimation
The PSM-based estimates of the ATT for our twelve outcomes of interest are reported
in Table 5. We summarise the results briefly. Matching confirms that today’s Soviet-era
22These numbers are based on calculations of the Rubin-Thomas B measure (Rubin and Thomas, 1996).
21
Science Cities are more innovative than historically similar localities: between 2006 and
2015 the former have applied for about 28 more patents at the EPO than the latter (10
patents with fractional counting). For both patent-based measures, the ATT does not
statistically differ from the “naive” raw mean difference calculated on the whole sample.
This is not the case, however, for other outcomes of interest. In fact, the 2010 popula-
tion of Science Cities is not statistically different from the one of matched control locali-
ties. On the other hand, Science Cities continue hosting a statistically significantly more
educated population: the college-educated share of their population is 7.4 percentage
points higher, and they also host more PhDs (the share is higher by about 0.3 percentage
points). The college educated share of population estimate is significantly lower than
the naive difference.
In terms of proxies for economic development, our results look as follows. The ATT
estimate for the night lights proxy is positive and statistically significant, with the point
estimate about a third of the size of the raw difference. Upon matching, the cities for
which information about SMEs is available do not seem to have more of these, neither
in absolute nor in relative terms. In fact, the estimated ATT for the absolute number of
SMEs is negative for both total economy and manufacturing, but only the latter is sta-
tistically significant (albeit at 10 per cent significance level). By contrast, raw differences
are positive and statistically significant. In addition, the estimated ATT for the number of
all SMEs relative to population is negative and statistically significant (about four SMEs
less per 1000 people); its equivalent for manufacturing is not statistically significant. Fi-
nally, and interestingly, the estimated ATT of SME labour productivity is positive and
statistically significant both overall and for manufacturing only, with point estimates
about a third lower than naive differences. The results seem to point to an economic
effect of Science Cities operating on the intensive (productivity) margin.
We also perform a sensitivity analysis of our ATT estimates, following Rosenbaum
(2002). Specifically, we simulate the presence of some unobserved factors that would
affect both the outcomes and the probability of receiving the treatment, and we assess
to what extent this would influence our conclusions about the presence of statistically
significant differences in Yi q between treated and (matched) control observations, for
all outcomes q = 1, . . . ,Q. The size of the simulated unobserved factor is given by pa-
rameter Γ≥ 1, which measures the hypothesised odds of receiving the treatment (Γ= 1
in an experimental setting). In Table 5 we report, for each outcome variable, the lower
value Γ∗ that leads to inconclusive tests about the presence of a statistically significant
22
difference between treated and control observations.23 The values of Γ∗ are very high
(above 2) for the patent outcomes, graduate and PhD shares, and for SME labour pro-
ductivity (general); they are higher than 1.5 but lower than 2 for the night lights and SME
labour productivity (manufacturing) outcomes; and below 1.5 for the other outcomes.24
These results are in line with our parametric tests on the estimated ATT parameters, and
demonstrate that our results are very robust to possible threats to identification.25
Finally, it must be mentioned that while our results are based on one-to-one match-
ing, the main qualitative conclusions are not altered in the case of one-to-many match-
ing. In fact, increasing the number of matched nearest neighbours usually increases bias
in exchange for a reduction in variance, and thus may result in a higher number of sta-
tistically significant ATT parameter estimates (possibly incorrectly). We have obtained
similar results by increasing the number of nearest neighbours up to five; however, we
do not present these results here due to space limitations.
6.2 Mahalanobis Matching: All Science Cities
A well-known critique of propensity score matching (King et al., 2011; King and Nielsen,
2016) is that while it may be effective at balancing the treated and matched control sam-
ples, actual pairs of observations with similar p-scores might be very different in their
covariates, biasing the resulting estimates. For this reason, it may be preferable to per-
form Mahalanobis matching or some variation of exact matching instead. On the other
hand, these procedures usually have stronger data requirements. In the context of this
paper, the debate acquires a geographical dimension: while we include polynomials of
municipal coordinates in models for p-score estimation, this solution might not be ef-
fective at matching historically similar places that are also close to each other in space. In
light of this, an alternative solution is to match directly on municipal coordinates, espe-
cially if one believes that relevant unobserved factors with a spatial dimension may be
23The full results of the sensitivity analysis are available upon request.24To provide context, Γ = 2 indicates a simulated unobserved factor that doubles the probability of re-
ceiving the treatment relative to that of not receiving it, or vice versa; such a high value of Γ would berealistic only in presence of very serious threats to our conditional independence assumption (1). Conse-quently, very high “critical” values of Γ∗ associated with a certain outcome – close to 2 or higher – indicatethat the results are likely to be very robust to such threats.
25At a first glance, it may seem counterintuitive that Γ∗ > 1 in the case of outcomes, such as populationin this setting, whose estimated ATT is not statistically different from zero. However, the latter is a conclu-sion derived from a parametric test, while the sensitivity analysis is based on non-parametric Wilcoxonsigned-rank tests. In practice, it is unlikely that the two procedures lead to very divergent conclusions.
23
an issue.
We compare our results obtained via one-to-one PSM with those based on one-to-
one matching on the Mahalanobis distance (2). As we discuss in Section 5, we calculate
the Mahalanobis distance by using the logarithm (and not the levels) of some asymmet-
rically distributed variables; and at the same time we force exact matching on specific
dummy variables (presence of railroads, rivers, lakes) whose corresponding distance
variables are excluded from the calculation of Mahalanobis distance (2). The result-
ing matched samples compare with those obtained via PSM as follows. We are unable
to obtain matches for three Science Cities in total (as opposed to four with PSM) in the
case of non-SME outcomes; while we give up a higher number of them, that is eight in
total (in contrast to two with PSM) in the case of SME outcomes. Because of stronger
data requirements and less flexibility of the method, covariate balance attained via Ma-
halanobis matching compares unfavorably with PSM.26 On the other hand, matched
municipalities are closer to each other, as displayed in Figure 5. The map shows that Ma-
halanobis matching is effective at finding adequately close matches also in areas with a
relatively high concentration of Science Cities, such as the Moscow region and the Urals.
Table 6 displays the empirical results obtained via Mahalanobis matching; estimates
of the ATT for each outcome without and with a bias adjustment term are shown in two
separate columns. The results are very similar to those obtained via PSM; here we high-
light few minor differences. First, the bias adjustment usually implies some reduction
of the point estimates (with the exception of patent outcomes), but no changes in terms
of qualitative conclusions about the statistical significance of the estimated parameters.
The effect on the number of SMEs in manufacturing is no longer statistically significant
in the case of Mahalanobis matching. The results from the sensitivity analysis are also
very similar to those in Table 5: critical values of Γ∗ are now higher for some outcomes
(patents), lower for others (graduate and PhD share, total economy SME labour produc-
tivity), substantially unchanged for the rest; qualitative conclusions are unchanged as
26Covariate balance statistics for Mahalanobis matching are reported in Table E.1 in Appendix E. Theyshow that, particularly for SME outcomes matching, some key covariates (the logs of historical popula-tion, of the number of plants, of municipal area) display a relatively large negative imbalance; that is, themeans for the matched control group are substantially larger in terms of standardised bias. By reasonablyassuming that these outcomes might have a positive effect on today’s outcomes (say, because of urbanpersistence and agglomeration effects in the case of past population, and because of the heritage of pastindustrialisation in the case of the number of plants) we can safely maintain that any bias on the resultingATT estimates due to such imbalance, if present, is negative; implying an interpretation in terms of “lowerbound” effects. In any case, the “bias adjustment” procedure should help mitigate such issues further.
24
the estimated effects that are statistically significant are also very robust. In general, we
interpret the fact that the two different procedures yield substantively similar results as
validation of the empirical strategy adopted in this study.
6.3 Matching Science Cities with no Current Official Status
A different concern about our empirical results is less methodological but more eco-
nomic and institutional in nature. As long as the results are at least in part driven by
Science Cities that have been receiving constant support from the Russian government
even in recent years (that is, some time after the dissolution of the USSR) it would be
difficult to interpret our estimates solely as “long-run” effects, as the results would bear
a mixed interpretation. At the extreme of the spectrum is a scenario in which the results
are only driven by continued funding, although in this case our estimates would still
be useful for a short-run “cost-benefit” analysis of a policy such as the establishment of
Science Cities.
In order to clear these doubts we perform a separate matching analysis which is
largely similar to the one discussed above, with the exception that we now remove Sci-
ence Cities that have the official status of Naukogrady in today’s Russia. As discussed
in section 2, funding for most Science Cities was discontinued after the breakup of the
USSR, and it was resumed only for a subset of them in the early 2000s: primarily the cur-
rent “official” Science Cities. By removing them from the sample, we can assess to a first
degree of approximation to what extent current funding is a factor behind our results.
We replicate both propensity score and Mahalanobis matching on the reduced sample;
in the case of PSM we perform separate propensity score estimation – for both groups
of outcomes – so as to select the models that attain the best covariate balance in the
reduced samples. Tables with covariate balance are provided in Appendix E.27
For brevity, we jump directly to the empirical estimates based on PSM and reported
in Table 7, which we compare directly with those from Table 5. We find the results strik-
ing. While the estimated ATT for the two patent outcomes register point estimates about
60 per cent smaller in both cases (but still statistically significant and robust to unob-
served factors, as given by Γ∗ > 2), all the other outcomes whose effects were previously
found to be positive and statistically significant remain so, with substantially unchanged
point estimates of the ATT parameters. The results based on Mahalanobis matching are
27Matching models are preliminary in both the PSM and Mahalanobis matching cases.
25
presented in Table 8, and display the same pattern. As in the PSM case, all statistically
significant coefficient estimates are very similar to the directly comparable ones in Ta-
ble 6, with the exception of patent outcomes, which are now smaller by 30-50 per cent.
It must be noted, however, that Mahalanobis matching results in a quite small matched
sample for the analysis of SME outcomes.
In short, the results are robust to excluding current official Science Cities from the
sample. The only difference is the reduction in the magnitude of estimated ATT for
patent applications abroad. While the latter finding is intuitive and can be attributed
to the absence of cities with direct government R&D funding, it also reinforces our belief
that the general results for other outcomes – graduate and PhD share, night lights, and
SME labour productivity – are to be interpreted as long-run effects. In fact, if on the con-
trary they were also dependent on current “hidden” funding, one would likely observe
a reduction in point estimates upon excluding current “official” Science Cities from the
sample.28 Still, this leaves open the question of what are the economic mechanisms
driving these results; we turn to this discussion next.
6.4 Mechanisms: Discussion
We summarise the results of our municipal-level analysis as follows. In today’s Rus-
sia, former Soviet-era Science Cities are not more populous than places that were in
most respects similar to them at the end of World War II. However, they host a more
educated population, and they seem to be more economically developed – as demon-
strated by our results on the night lights variable as well as on the two measures of
SME labour productivity. The economic effect of Science Cities occurs on the inten-
sive (rather than extensive) margin, in light of our results on the absolute and relative
numbers of SMEs registered in these municipalities (that is, Science Cities are not much
more “entrepreneurial” than comparable places). In other words, it is a productivity ef-
fect. In terms of innovation, Science Cities apply for more patents abroad, although the
effect is largely driven by the few localities for which the Russian government resumed
funding in the early 2000s.
Having ruled out current funding as a major potential mechanism driving the effects,
28Symmetrically, if the positive albeit smaller effects associated with the patent outcomes in Table 7only depended on the persistence of governmental support for some of today’s non-official Science Cities,and the same mechanism is also hypothesised to affect other outcomes, one would expect higher effectsassociated with the latter in the main results from Table 5.
26
we interpret the results in light of the theoretical contributions cited in the introduction
(Glaeser and Gottlieb, 2009; Kline and Moretti, 2014b). The theory of place-based poli-
cies postulates that long-run effects are only present after the intervention expires as
long as the latter is somehow able to shift economic activity onto a different, “better”
equilibrium. In turn, this is possible if positive agglomeration effects (such as localised
knowledge spillovers) are sufficiently non-linear, as otherwise the prevalence of con-
gestion effects would induce out-migration of workers. In the unique context of Soviet
Science Cities, where the policy specifically targeted R&D personnel, this corresponds
to a scenario in which the policy was initially successful at concentrating human capi-
tal in selected locations; after the suspension of the policy following the dissolution of
the USSR, these human capital differentials persisted and may have translated into a
productivity advantage even through knowledge spillover effects.
This interpretation, however, leaves some questions unanswered while at the same
time raising others. In light of the reduced form evidence, it is at present impossible to
disentangle to what extent the productivity and economic results are a mere “return to
human capital” effect (more educated people make better entrepreneurs), and to what
extent there are any spillover effects (externalities). Looking through the theoretical lens,
the same spatial equilibrium might as well have arisen because of barriers to individual
mobility – unemployed scientists having no other option but to stay where they were and
become entrepreneurs. Existing evidence suggests that this may have been the case: in-
ternal migration rates in Russia in the 1990s were low (see Andrienko and Guriev (2004);
Friebel and Guriev (2005)). Russian anecdotal evidence provides plenty of examples of
both scientists who emigrated and others who became entrepreneurs following the dis-
solution of the USSR, although as discussed in section 2.3, emigration accounted for less
than 3 per cent of the total outflow of staff from the R&D sector. Studying the issue in
more depth is an interesting subject for future work, assuming data exist and can be
accessed.
7 Empirical Results at the Firm Level
In this section we discuss the empirical results at the firm level, assessing the “spatial
reach” of Science Cities as expressed by our hypothesis HF.1. We separately focus on
probit estimates of binary innovation outcomes, and on OLS regressions featuring con-
tinuous measures of firm performance on the left hand side. In addition, we provide a
27
brief discussion of these results in light of those from the municipal-level analysis.
7.1 Binary Innovation Outcomes
Table 9 presents the results from the estimation of probit model (4) for four separate
firm-level outcome binary outcomes I f r : whether a firm engages in any R&D activity,
whether in the three years prior to the survey the firm has introduced either a product
or a process new to the firm or beyond, and finally looking at product and process inno-
vation separately. In addition to our measure of distance of the firm from Science Cities,
on the right-hand side of the model we include a set of controls specified in section 5.2.
We cluster standard errors at the municipal level.
The estimates of our coefficient of interest β are as follows: negative and not statis-
tically different from zero for engaging in R&D, positive and statistically different from
zero (at the 5 per cent significance level) for engaging in either product or process in-
novation (or both), and finally positive but not statistically significant from zero for en-
gaging in product and process innovation separately.29 In short, there is mixed evidence
of a spatial correlation between the location of science cities and firm-level innovation:
while firms closer to a Science City are more likely to innovate, the β coefficients for the
two “disaggregated” outcomes from columns (3) and (4) of the table are not precisely
estimated.
While it is possible to find probabilistic explanations of these results favourable to
our initial hypothesis – say, statistical tests of this relationships may have too low of a
power if one restricts the analysis to specific types of innovation – one could also see the
significant β parameter from column (2) itself as a mere random occurrence. Taken at
face value, however, that estimated coefficient is economically significant. Back-of-the-
envelope calculations of the marginal effect from column (2) suggest that if the average
firm were to move from an area with no Science Cities to a Science City, the likelihood
that it would engage in (product or process) innovation would increase by 17.7 per cent
29As for the other explanatory variables for firm innovation, Table 9 indicates that larger and older firmsare more likely to innovate (perhaps because newer firms start closer to the technological frontier), andso are firms in manufacturing and the ones employing a relatively more skilled labour force. While firmswho mostly trade on the national market are more likely to conduct R&D and introduce new productsand processes, those whose market is more local are more likely to engage in process innovation. Firmswhose main market is international, are more likely to invest in R&D and engage in product innovation;being controlled by the state seems to reduce the likelihood to engage in innovation; being controlled byforeign owners, on the other hand, seems to have no effect at all.
28
(with 3.6 per cent p-value).30 This effect may be driven by firms that engage in product
and process innovation simultaneously, rather than separately in either of the two. This
conclusion, however, implicitly assumes that there are no unobserved determinants of
innovation that correlate with the observed location of cities and that are not appropri-
ately accounted for by our set of controls.
7.2 Other Continuous Outcomes
Table 10 reports estimates of model (5) for three firm-level outcomes: log operating rev-
enue (also controlling for conventional production inputs), log labour productivity, and
the share of firm sales that are due to innovative products. As above, we include addi-
tional controls, region fixed effects, and cluster standard errors at the municipal level. In
all three regressions, the estimated β coefficient is negative and not statistically differ-
ent from zero; that is, there is no association between the performance of firms and their
proximity to Science Cities. These more negative results could be reconciled with those
relative to firm-level innovation by postulating a weak relationship between innovation
and performance of Russian firms, possibly due to institutional barriers hampering the
growth of more dynamic Russian enterprises.
7.3 Results: Discussion
Our firm-level results indicate that there is mixed evidence of a spatial relationship be-
tween the location of Science Cities and Russian firms most inclined to engage in inno-
vation, coupled with a decidedly unfavourable evidence for the existence of an analo-
gous link with measures of firm performance. In light of the results from our municipal-
level analysis, we argue that the scope of Science Cities’ effects on innovation and eco-
nomic outcomes are limited in space, which is consistent with fast distance decay of
knowledge spillovers as evidenced in prior studies, such as (Lychagin et al., 2016). They
may also be specific to certain sectors within that limited space, rather than benefitting
firms in any sector. More research is certainly needed for a better understanding of the
spatial extent of knowledge flows, with particular reference to the Russian context.
30We estimate this as the change in the likelihood in engaging in some kind of innovation that is asso-ciated with increasing the main regressor
∑Ss=1 exp
[−Dist(
f , s)]
in (4) from 0 to 1, and we calculate theassociated standard error via a Delta Method approximation. Both this figure and its attached interpreta-tion depend on the functional form assumption of the probit model (4).
29
8 Conclusion
In this paper we have analysed the long-run effects of a historical placed-based policy:
the creation of R&D-focused Science Cities in Soviet Russia. Both the initial establish-
ment and the eventual partial suspension of this program was largely guided by political
factors that are arguably exogenous to drivers of current social and economic conditions
of Russian cities. We compare municipalities with Science Cities to other municipalities
that were observationally similar to them at the time of their selection, and we compute
differences in the current characteristics between the two groups. We find that the mu-
nicipalities with former Science Cities, while not significantly larger, still host a more
educated population today, apply for more patents abroad, and host more productive
SMEs (although not a higher number of them). With a separate firm-level analysis, how-
ever, we can attest only limited evidence in support of the hypothesis that the effect of
Science Cities extends beyond the municipal borders.
Because our results remain unchanged even after excluding municipalities with Sci-
ence Cities that still receive support by the Russian government from the sample, we
conjecture that they are primarily driven by the local persistence of a more educated
population, an institutional heritage from the Soviet era. With the information currently
at our disposal, we are unable to distinguish whether this is mostly caused by barriers to
labour mobility, or by localised knowledge spillovers that would, on one hand, change
the spatial equilibrium and, on the other hand, contribute directly to the observed ef-
fects, though there is some evidence suggesting there were barriers to labour mobility.
We leave the exploration of this problem to future work. In either case, we believe that
ours is a valuable contribution to the existing literature on place-based policies, which
up to now has found only limited evidence in favour of long-run effects following the
suspension of a program. More generally, our results are also informative for science
and innovation policy, both in the context of emerging economies such as Russia as well
as developed economies.
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Figures
Figure 1: Location of science cities and regions covered in BEEPS V Russia
Source: Table A.1 and BEEPS V Russia.
Figure 2: Gross expenditures on R&D (GERD) in Russia, 1989-2010
Source: Gokhberg (1997), Russian Statistical Yearbooks (various years) and OECD MainScience and Technology Indicators (MSTI) database.
38
Figure 3: Researchers in Russia, 1989-2010
Source: Gokhberg (1997), Russian Statistical Yearbooks (various years) and OECD MainScience and Technology Indicators (MSTI) database.
Figure 4: Defense R&D in Russia and elsewhere, 1994-2003
Source: Gokhberg (1997) and OECD Main Science and Technology Indicators (MSTI)database.
39
Figure 5: Science Cities and their matches
(a) Propensity score matching
(b) Mahalanobis matching
Source: Authors’ calculations.
40
Tables
Table 1: Municipal-level data: Descriptive statistics
Municipalities Municipalitieswith Science Cities without Science CitiesObs. Mean (SE) Obs. Mean (SE) p-value
Latitude 88 55.664 2250 53.981 0.000(0.391) (0.108)
Longitude 88 49.771 2250 59.955 0.000(2.387) (0.620)
Average temperature in January (°C) 88 -11.632 2250 -13.559 0.000(0.410) (0.149)
Average temperature in July (°C) 88 18.535 2250 18.755 0.247(0.181) (0.056)
Average altitude 88 0.169 2250 0.267 0.000(0.010) (0.007)
Minimum distance from railroad 88 0.007 2250 0.078 0.000(0.001) (0.005)
Minimum distance from river 88 0.032 2250 0.056 0.000(0.004) (0.001)
Minimum distance from lake 88 0.118 2250 0.172 0.000(0.009) (0.003)
Minimum distance from coast 88 0.725 2250 0.730 0.917(0.044) (0.010)
Minimum distance from USSR border 88 0.665 2250 0.679 0.723(0.037) (0.009)
Population in 1959 88 67.583 2250 49.573 0.167(12.516) (3.242)
Number of universities in 1959 88 0.557 2250 0.196 0.132(0.224) (0.046)
Number of plants in 1947 88 6.205 2250 2.484 0.023(1.458) (0.697)
Number of R&D institutes in 1959 88 0.807 2250 0.412 0.242(0.253) (0.222)
Area in km2 88 0.692 2250 7.108 0.000(0.116) (0.637)
Patent applications, 2006-2015, simple 88 37.875 2250 6.025 0.000(8.033) (3.375)
Patent applications, 2006-2015, fractional 88 13.909 2250 2.265 0.002(3.489) (1.210)
Night lights, 2009-2011 88 30.611 2250 7.638 0.000(2.124) (0.272)
Population in 2010 88 131.557 2250 58.324 0.001(21.169) (5.871)
Graduate share in 2010 88 0.225 2250 0.110 0.000(0.008) (0.001)
PhD share in 2010 88 0.006 2250 0.003 0.000(0.000) (0.000)
Number of SMEs in 2010 (thousands, all) 69 3239.725 2140 1189.833 0.008(742.669) (67.367)
SMEs per 1000 people (all) 69 0.025 2159 0.027 0.086(0.001) (0.000)
SME labor productivity (all) 69 1643.995 2153 794.105 0.000(84.513) (9.213)
Number of SMEs in 2010 (thousands, manufacturing) 69 395.073 2038 119.546 0.010(103.133) (7.535)
SMEs per 1000 people (manufacturing) 69 0.002 2038 0.002 0.066(0.000) (0.000)
SME labour productivity (manufacturing) 67 1438.443 2014 768.462 0.000(84.554) (20.805)
41
Table 2: Firm-level data: Descriptive statistics
Obs Mean Std. dev. Min MaxYoung firms (0-5 years) 4220 0.297 0.457 0 1
50% or more foreign owned 4220 0.026 0.158 0 1
50% or more state owned 4220 0.012 0.108 0 1
Direct exporter 4220 0.100 0.300 0 1
Main market: local 4220 0.697 0.460 0 1
Main market: national 4220 0.288 0.453 0 1
% of employees with a completed university degree 4045 52.505 30.521 0 100
Manufacturing 4220 0.325 0.469 0 1
Located in a city with population over 1 million 4220 0.255 0.436 0 1
Minimum distance of a firm from a Science City (in km) 4220 154.410 237.742 0.198 1358.035
Exponential 1 decay distance of a firm from a Science City 4220 0.012 0.067 0.000 0.822
Log (number of employees), Orbis 2979 3.750 0.860 0.693 8.860
Log (capital), Orbis 3027 5.603 2.089 -2.659 12.916
Log (materials), Orbis 2936 6.212 1.964 -3.912 12.839
Log (operating revenue), Orbis 2980 6.442 1.862 -1.966 13.036
Total factor productivity 2979 0.079 1.356 -8.194 6.544
Log labour productivity, Orbis 2979 2.690 1.343 -5.577 9.167
R&D (dummy) 4220 0.111 0.314 0 1
% of innovative products in sales 4101 3.855 13.937 0 100
Technological innovation (dummy) 4220 0.216 0.411 0 1
Product innovation (dummy) 4220 0.129 0.335 0 1
Process innovation (dummy) 4220 0.141 0.348 0 1
Source: Authors’ calculations based on BEEPS V and Bureau Van Dijk’s Orbis.
42
Table 3: Propensity Score Models (Probit Estimates)
(1): Model for (2): Model forNon-SME outcomes SME outcomes
Latitude 1.105∗∗∗ (0.409) 0.225∗∗ (0.110)Latitude (Sq.) -0.007∗∗ (0.003)Longitude 0.083 (0.053) -0.056 (0.091)Longitude (Sq.) 0.000 (0.000)Latitude × Longitude -0.001 (0.001) 0.001 (0.001)Average temperature in January (°C) 0.246∗∗∗ (0.070) 0.098 (0.119)Average temperature in January (°C) (Sq.) -0.004 (0.004)Average temperature in July (°C) 0.203∗ (0.114) 0.737 (0.475)Average temperature in July (°C) (Sq.) -0.013 (0.011)Average altitude 2.434∗∗ (0.983) 3.139∗ (1.783)Average altitude (Sq.) -1.278 (1.931)Minimum distance from railroad -3.981 (14.767) -7.873 (14.053)Minimum distance from railroad (Sq.) -273.8 (326.7) -195.2 (303.4)Minimum distance from river -3.279∗ (1.826) -7.947∗∗ (3.610)Minimum distance from river (Sq.) 31.481 (21.661)Minimum distance from lake 1.592 (1.915) 2.048 (1.899)Minimum distance from lake (Sq.) -7.449 (5.281) -9.541∗ (5.271)Minimum distance from coast -0.739∗ (0.396) -0.267 (0.404)Minimum distance from USSR border 1.285 (0.797) 0.162 (0.351)Minimum distance from USSR border (Sq.) -0.660 (0.458)Population in 1959 -0.003∗ (0.002) -0.003∗ (0.002)Number of universities in 1959 -0.231 (0.155) 0.001 (0.085)Number of universities in 1959 (Sq.) 0.029∗∗ (0.014)Number of plants in 1947 0.046∗∗ (0.021) 0.016 (0.014)Number of plants in 1947 (Sq.) -0.001∗∗ (0.000)Number of R&D institutes in 1959 0.146∗ (0.080) 0.208∗∗∗ (0.079)Number of R&D institutes in 1959 (Sq.) -0.002 (0.003) -0.005∗ (0.003)Area in km2 -0.342∗∗∗ (0.063) -0.318∗∗∗ (0.063)Area in km2 (Sq.) 0.001∗ (0.001)Constant -43.729∗∗∗ (14.945) -21.644∗∗ (9.765)Log-Likelihood -264.23 -268.87Pseudo R2 0.2953 0.2829Model χ2 (p-value) 221.42 (0.0000) 212.13 (0.0000)Observations 2338 2338
Notes: ∗ denotes p < 0.10, ∗∗ denotes p < 0.05, and ∗∗∗ denotes p < 0.01; where p is the p-valueassociated to each parameter estimate (standard errors are reported in parentheses). The fol-lowing variables, along with the associated squared terms (if present), enter the model multi-plied by a scale factor of 10−3: average altitude; minimum distances from any railroad, river,lake, coast, the USSR border; population in 1959; area in km2.
43
Table 4: Covariate Balance: Propensity Score Matching, all Science Cities
Matched Sample: non-SME OutcomesVariable Treated Control Std. bias t-stat. p-value V-ratioLatitude 55.664 55.871 -5.4 -0.36 0.722 0.94Longitude 49.771 49.861 -0.4 -0.03 0.979 0.92Average temperature in January (°C) -11.632 -11.706 1.8 0.12 0.905 0.83Average temperature in July (°C) 18.535 18.424 6.1 0.40 0.689 0.90Average altitude 0.1686 0.1706 -1.8 -0.12 0.908 0.63Minimum distance from railroad 0.0068 0.0074 -5.8 -0.38 0.707 1.08Minimum distance from river 0.0319 0.0312 2.2 0.14 0.886 1.39Minimum distance from lake 0.1184 0.1185 -0.1 -0.01 0.995 0.94Minimum distance from coast 0.7246 0.7221 0.6 0.04 0.969 0.86Minimum distance from USSR border 0.6651 0.6404 6.9 0.45 0.650 0.70Population in 1959 67.583 65.933 1.5 0.10 0.922 1.44Number of universities in 1959 0.5568 0.5238 1.7 0.11 0.914 1.31Number of plants in 1947 6.2045 5.9286 2.1 0.14 0.889 1.34Number of R&D Institutes in 1959 0.8068 0.6786 5.5 0.36 0.719 1.31Area in km2 0.6923 0.7791 -8.6 -0.56 0.574 1.08
Matched Sample: SME OutcomesVariable Treated Control Std. bias t-stat. p-value V-ratioLatitude 55.664 56.027 -9.6 -0.59 0.555 0.95Longitude 49.771 48.819 4.3 0.26 0.792 0.99Average temperature in January (°C) -11.632 -11.714 2.1 0.13 0.896 1.01Average temperature in July (°C) 18.535 18.445 5.3 0.32 0.746 1.04Average altitude 0.1686 0.1666 1.9 0.12 0.904 0.78Minimum distance from railroad 0.0068 0.0064 5.3 0.32 0.747 1.63Minimum distance from river 0.0319 0.0325 -1.9 -0.12 0.906 1.46Minimum distance from lake 0.1184 0.1221 -4.0 -0.25 0.803 0.81Minimum distance from coast 0.7246 0.7124 3.1 0.19 0.850 1.15Minimum distance from USSR border 0.6651 0.6829 -5.0 -0.31 0.758 0.94Population in 1959 67.583 76.074 -7.4 -0.46 0.650 1.65Number of universities in 1959 0.5568 0.5970 -2.1 -0.13 0.898 1.82Number of plants in 1947 6.2045 6.8806 -5.1 -0.31 0.754 1.50Number of R&D institutes in 1959 0.8068 0.7761 1.4 0.09 0.930 1.63Area in km2 0.6923 0.7183 -2.6 -0.16 0.875 1.32
Notes: For each variable, the table reports its mean for both the treated and control groups of thematched sample in question, along with the corresponding standardised bias (the treated-control dif-ference as a percentage of the combined standard deviation of the matched sample). In addition, foreach variable the table reports the t-statistic and the associated p-value for the difference in the twomeans, as well as the ratio between the variances of the treated and the variance of the control groupfor the variable in question. Both matched samples are obtained via 1:1 nearest neighbour matching onthe estimated propensity score (with replacement).
44
Table 5: Municipal-level Analysis: Propensity Score Matching, all Science Cities
Whole sample Matched sample (1 nearest neighbour)
Outcome Raw difference T C ATT Γ∗ (α= .05)
Patent applications, simple31.850***
84 7528.167***
2.80(8.675) (7.352)
Patent applications, fractional11.644***
84 759.953***
2.55(3.676) (3.336)
Night lights, 2009-201122.973***
84 757.587***
1.55(2.130) (2.271)
Population in 195973.233***
84 7532.164
1.20(21.861) (25.861)
Graduate share in 20100.115***
84 750.074***
5.55(0.008) (0.008)
PhD share in 20100.003***
84 750.003***
2.75(0.000) (0.000)
Number of SMEs in 2010 (thousands, all)2.050***
67 56-0.907
1.00(0.740) (0.827)
Number of SMEs in 2010 (thousands, manufacturing)0.275***
67 56-0.254*
1.35(0.103) (0.131)
SMEs per 1000 people (all)-2.042*
67 56-4.372***
1.20(1.222) (1.557)
SMEs per 1000 people (manufacturing)0.240*
67 560.077
1.00(0.128) (0.130)
SME labour productivity (all)0.850***
67 560.526***
4.10(0.084) (0.068)
SME labour productivity (manufacturing)0.670***
67 560.486***
1.65(0.086) (0.105)
Notes: ∗ denotes p < 0.10, ∗∗ denotes p < 0.05, and ∗∗∗ denotes p < 0.01; where p is the p-value associated to each param-eter estimate (standard errors are reported in parentheses). In the matched sample, T is the number of matched treatedobservations; C is the number of matched controls; ‘ATT’ is the estimate of (3), where standard errors are computed fol-lowing Abadie and Imbens (2006) and account for the fact that the propensity score is estimated; while Γ∗ is the minimumvalue of parameter Γ ≥ 1, selected from a grid spaced by intervals of 0.05 length, such that in a sensitivity analysis à laRosenbaum (2002) the set of Wilcoxon signed-rank tests associated with Γ∗ do not simultaneously reject the null hypoth-esis that the outcome variable is not different across the treated and control samples, for tests with α= 0.05 type I error. Ahigher value of Γ is associated to a stronger simulated unobserved factor which affects both the outcome and the proba-bility of receiving the treatment. Complete results of the sensitivity analysis for specific outcomes and for different valuesof Γ are available upon request.
45
Table 6: Municipal-level Analysis: Mahalanobis Matching, all Science Cities
Whole sample Matched sample (1 nearest neighbour)
Outcome Raw difference T C ATT ATT b.a. Γ∗ (α= .05)
Patent applications, simple31.850***
85 7029.835*** 28.556***
6.25(8.675) (7.345) (7.215)
Patent applications, fractional11.644***
85 7011.138*** 10.672***
5.55(3.676) (3.352) (3.330)
Night lights22.973***
85 707.234*** 5.512***
1.60(2.130) (2.147) (2.034)
Population in 201073.233***
85 7024.486* 18.891
1.90(21.861) (13.157) (11.860)
Graduate share in 20100.115***
85 700.064*** 0.055***
3.95(0.008) (0.012) (0.011)
PhD share in 20100.003***
85 700.002*** 0.002***
2.05(0.000) (0.000) (0.000)
Number of SMEs (thousands, all)2.050***
61 520.238 0.230
1.20(0.740) (0.349) (0.362)
Number of SMEs (thousands, manufacturing)0.275***
61 520.077 0.062
1.20(0.103) (0.069) (0.064)
SMEs per 1000 people (all)-2.042*
61 52-2.046 -2.975*
1.00(1.222) (1.777) (1.841)
SMEs per 1000 people (manufacturing)0.240*
61 52-0.181 -0.303
1.00(0.128) (0.230) (0.237)
SME labour productivity (All)0.850***
61 520.420*** 0.372***
2.85(0.084) (0.085) (0.085)
SME labour Productivity (manufacturing)0.670***
61 520.311** 0.253**
1.65(0.086) (0.122) (0.119)
Notes: ∗ denotes p < 0.10, ∗∗ denotes p < 0.05, and ∗∗∗ denotes p < 0.01; where p is the p-value associated to each parameterestimate (standard errors are reported in parentheses). In the matched sample, T is the number of matched treated observa-tions; C is the number of matched controls; ‘ATT’ and ‘ATT b.a.’ are two estimates of (3) respectively excluding and includinga bias-adjustment term as per Abadie and Imbens (2011) (in both cases, standard errors are computed following Abadie andImbens (2006)); while Γ∗ is the minimum value of parameter Γ ≥ 1, selected from a grid spaced by intervals of 0.05 length,such that in a sensitivity analysis à la Rosenbaum (2002) the set of Wilcoxon signed-rank tests associated with Γ∗ do not si-multaneously reject the null hypothesis that the outcome variable is not different across the treated and control samples, fortests with α = .05 type I error. A higher value of Γ is associated to a stronger simulated unobserved factor which affects boththe outcome and the probability of receiving the treatment. Complete results of the sensitivity analysis for specific outcomesand for different values of Γ are available upon request.
46
Table 7: Municipal-level Analysis: Propensity Score Matching, excluding current Science Cities
Whole sample Matched sample (1 nearest neighbour)
Outcome Raw difference T C ATT Γ∗ (α= .05)
Patent applications, simple23.448***
70 6313.729***
2.65(8.978) (3.209)
Patent applications, fractional7.254***
70 634.067***
2.35(2.703) (0.811)
Night lights20.101***
70 635.870**
1.25(2.318) (2.597)
Population in 201082.854***
70 6325.783
1.50(25.398) (24.905)
Graduate share in 20100.103***
70 630.063***
3.05(0.009) (0.010)
PhD share in 20100.003***
70 630.002***
2.15(0.000) (0.000)
Number of SMEs (thousands, all)2.356***
54 490.576
1.15(0.900) (0.800)
Number of SMEs (thousands, manufacturing)0.314**
54 490.109
1.45(0.125) (0.131)
SMEs per 1000 people (all)-2.639*
54 49-1.538
1.15(1.347) (1.848)
SMEs per 1000 people (manufacturing)0.095
54 49-0.101
1.00(0.128) (0.233)
SME labour productivity (all)0.799***
54 490.467***
2.45(0.097) (0.104)
SME labour productivity (manufacturing)0.636***
54 490.506***
1.35(0.100) (0.109)
Notes: ∗ denotes p < 0.10, ∗∗ denotes p < 0.05, and ∗∗∗ denotes p < 0.01; where p is the p-value associated toeach parameter estimate (standard errors are reported in parentheses). In the matched sample, T is the numberof matched treated observations; C is the number of matched controls; ‘ATT’ is the estimate of (3), where standarderrors are computed following Abadie and Imbens (2006) and account for the fact that the propensity score is esti-mated; while Γ∗ is the minimum value of parameter Γ≥ 1, selected from a grid spaced by intervals of 0.05 length,such that in a sensitivity analysis à la Rosenbaum (2002) the set of Wilcoxon signed-rank tests associated with Γ∗do not simultaneously reject the null hypothesis that the outcome variable is not different across the treated andcontrol samples, for tests with α= 0.05 type I error. A higher value of Γ is associated to a stronger simulated unob-served factor which affects both the outcome and the probability of receiving the treatment. Complete results ofthe sensitivity analysis for specific outcomes and for different values of Γ are available upon request.
47
Table 8: Municipal-level Analysis: Mahalanobis Matching, excluding current Science Cities
Whole sample Matched sample (1 nearest neighbour)
Outcome Raw difference T C ATT ATT b.a. Γ∗ (α= .05)
Patent applications, simple23.448***
71 6120.127*** 19.026***
2.75(8.978) (7.282) (6.982)
Patent applications, fractional7.254***
71 616.152*** 5.706***
2.55(2.703) (1.876) (1.773)
Night lights20.101***
71 614.995** 3.141
2.15(2.318) (2.202) (1.975)
Population in 201082.854***
71 6120.893 16.240
1.80(25.398) (15.016) (13.030)
Graduate share in 20100.103***
71 610.052*** 0.045***
4.65(0.009) (0.010) (0.010)
PhD share in 20100.003***
71 610.002*** 0.002***
2.00(0.000) (0.000) (0.000)
Number of SMEs (thousands, all)2.356***
45 370.180 0.008
2.05(0.900) (0.683) (0.606)
Number of SMEs (thousands, manufacturing)0.314**
45 370.118 0.103
1.65(0.125) (0.099) (0.094)
SMEs per 1000 people (all)-2.639*
45 37-1.173 -1.947
1.00(1.347) (1.660) (1.801)
SMEs per 1000 people (manufacturing)0.095
45 37-0.376 -0.302
1.00(0.128) (0.250) (0.257)
SME labour productivity (all)0.799***
45 370.318*** 0.329***
3.00(0.097) (94.352) (91.068)
SME labour productivity (manufacturing)0.636***
45 370.214 0.250*
2.30(0.100) (0.131) (0.128)
Notes: ∗ denotes p < 0.10, ∗∗ denotes p < 0.05, and ∗∗∗ denotes p < 0.01; where p is the p-value associated to each parameterestimate (standard errors are reported in parentheses). In the matched sample, T is the number of matched treated observa-tions; C is the number of matched controls; ‘ATT’ and ‘ATT b.a.’ are two estimates of (3) respectively excluding and includinga bias-adjustment term as per Abadie and Imbens (2011) (in both cases, standard errors are computed following Abadie andImbens (2006)); while Γ∗ is the minimum value of parameter Γ ≥ 1, selected from a grid spaced by intervals of 0.05 length,such that in a sensitivity analysis à la Rosenbaum (2002) the set of Wilcoxon signed-rank tests associated with Γ∗ do not si-multaneously reject the null hypothesis that the outcome variable is not different across the treated and control samples, fortests with α= 0.05 type I error. A higher value of Γ is associated to a stronger simulated unobserved factor which affects boththe outcome and the probability of receiving the treatment. Complete results of the sensitivity analysis for specific outcomesand for different values of Γ are available upon request.
48
Table 9: Firm-level Analysis: Probit estimation of model with latent variable representation (4),for different outcomes I f r
(1) (2) (3) (4)Technological Product Process
Does R&D innovation innovation innovationβ Coefficient -0.354 1.170∗∗ 0.912 0.520
(0.982) (0.586) (0.743) (0.804)
(Log) employees 0.296∗∗∗ 0.285∗∗∗ 0.221∗∗∗ 0.226∗∗∗(0.056) (0.042) (0.049) (0.045)
Young firms (0-5 years) -0.036 -0.321∗∗∗ -0.317∗∗∗ -0.330∗∗∗(0.117) (0.097) (0.112) (0.103)
50% or more foreign owned -0.167 -0.016 -0.311 -0.049(0.348) (0.350) (0.429) (0.434)
50% or more state owned -0.718 -0.961∗∗∗ -0.120 -1.559∗∗∗(0.533) (0.359) (0.411) (0.526)
Direct exporter 0.702∗∗∗ 0.231 0.429∗∗ 0.099(0.205) (0.156) (0.176) (0.160)
Main market: local 0.131 0.675∗ 0.336 1.354∗∗(0.348) (0.377) (0.420) (0.683)
Main market: national 1.038∗∗∗ 1.122∗∗∗ 0.772∗ 1.779∗∗∗(0.313) (0.391) (0.430) (0.676)
% of employees with a 0.012∗∗∗ 0.005∗∗∗ 0.006∗∗∗ 0.003∗completed university degree (0.002) (0.002) (0.002) (0.002)
Manufacturing 1.018∗∗∗ 1.424∗∗∗ 1.501∗∗∗ 1.299∗∗∗(0.135) (0.094) (0.118) (0.102)
Located in a city with -0.477∗∗ -0.148 -0.148 -0.003population over 1 million (0.211) (0.169) (0.188) (0.167)Region fixed effects YES YES YES YESObservations 4040 4040 4040 4040
Notes: ∗ denotes p < 0.10, ∗∗ denotes p < 0.05, and ∗∗∗ denotes p < 0.01; where p is thep-value associated to each parameter estimate (standard errors are reported in parenthe-ses). All models include fixed effects for the Russian regions at the oblast administrativelevel. Standard errors are clustered at the municipal (rayon) level.
49
Table 10: Firm-level Analysis: OLS estimation of Equation (5) for different outcomes P f r
(1) (2) (3)(Log) sales Log(Y/L) Inn. share
of salesβ Coefficient -0.080 -0.491 -2.618
(0.132) (0.498) (3.821)
(Log) employees 0.173∗∗∗ 0.294(0.029) (0.252)
(Log) capital 0.126∗∗∗(0.017)
(Log) materials 0.752∗∗∗(0.031)
Young firms (0-5 years) 0.072∗∗ 0.089 -0.825∗(0.029) (0.060) (0.448)
50% or more foreign owned 0.055 0.551∗∗∗ 1.370(0.045) (0.183) (2.096)
50% or more state owned -0.185∗ -0.241 -1.082(0.098) (0.270) (2.955)
Direct exporter 0.043 0.627∗∗∗ 1.566∗(0.027) (0.079) (0.854)
Main market: local 0.094 0.130 2.425(0.058) (0.322) (2.296)
Main market: national 0.082 0.465 4.489∗(0.061) (0.324) (2.397)
% of employees with a 0.001∗∗∗ -0.001 0.024∗∗∗completed university degree (0.000) (0.001) (0.007)
Manufacturing -0.134∗∗∗ -0.167∗∗∗ 4.090∗∗∗(0.019) (0.046) (0.645)
Located in a city with 0.025 0.039 -1.212population over 1 million (0.046) (0.122) (1.152)Region fixed effects YES YES YESObservations 2809 2856 3933
Notes: ∗ denotes p < 0.10, ∗∗ denotes p < 0.05, and ∗∗∗ denotes p <0.01; where p is the p-value associated to each parameter estimate(standard errors are reported in parentheses). All models include fixedeffects for the Russian regions at the oblast administrative level. Stan-dard errors are clustered at the municipal (rayon) level. Differencesin sample size across models reflect differences in coverage about ac-counting data (employees, capital, materials).
50
ASc
ien
ceC
itie
sin
the
Sovi
etU
nio
nan
dR
uss
ia
Tab
leA
.1:S
cien
ceci
ties
Nau
kogr
adC
lose
dci
tyd
Pri
ori
tysp
ecia
lisa
tio
nar
easa
No.
Lo
cati
on
aO
bla
st
Foundede
YearSovietstatuse
YearRussianstatusf
Typea
Pas
tN
ow
Militarybase
Airrocketandspace
research
Electronicsandradio
engineering
Automation,ITand
instrumentation
Chemistry,chemicalphysics
andnewmaterials
Nuclearcomplex
Energetics
Biology,biotechnologyand
agriculturalsciences
1B
iysk
Alt
aiK
rai
1718
1957
2005
1N
oN
oN
oN
oN
oN
oYe
sN
oN
oYe
s
2M
irn
yA
rkh
ange
lsk
1957
1966
2Ye
sYe
sN
oYe
sN
oN
oN
oN
oN
oN
o
3Se
vero
dvi
nsk
Ark
han
gels
k19
3619
392
Yes
No
Yes
No
No
Yes
No
No
No
No
4Z
nam
ensk
Ast
rakh
an19
4819
622
Yes
Yes
Yes
Yes
No
No
No
No
No
No
5M
iass
Ch
elya
bin
sk17
7319
551
No
No
No
Yes
No
Yes
No
No
No
No
6O
zyo
rsk
Ch
elya
bin
sk19
4519
452
Yes
Yes
No
No
No
No
No
Yes
No
No
7Sn
ezh
insk
Ch
elya
bin
sk19
5719
572
Yes
Yes
No
No
No
No
No
Yes
No
No
8Tr
yokh
gorn
yC
hel
yab
insk
1952
1952
2Ye
sYe
sN
oN
oN
oYe
sN
oYe
sN
oN
o
9U
st-K
atav
Ch
elya
bin
sk17
5819
421
No
No
No
Yes
No
Yes
No
No
No
No
10K
asp
iysk
cD
ages
tan
1932
1936
2N
oN
oN
oN
oN
oYe
sN
oN
oN
oN
o
11A
kad
emgo
rod
ok
Irku
tsk
1949
1988
5N
oN
oN
oYe
sYe
sYe
sYe
sYe
sYe
sYe
s
12A
nga
rskc
Irku
tsk
1948
1957
2N
oN
oN
oN
oN
oN
oYe
sYe
sN
oN
o
13O
bn
insk
Kal
uga
1946
1946
2000
2N
oN
oN
oN
oN
oYe
sN
oYe
sYe
sN
o
14So
sen
skyc
Kal
uga
1952
1973
3N
oN
oN
oN
oN
oYe
sN
oN
oN
oN
o
15K
om
som
ols
k-o
n-A
mu
rK
hab
arov
sk19
3219
341
No
No
No
Yes
No
Yes
No
No
No
No
16K
rasn
od
ar-5
9bK
rasn
od
arN
oN
oN
oN
oN
oN
oN
oN
oN
o
17A
kad
emgo
rod
ok
Kra
snoy
arsk
1944
1965
5N
oN
oN
oYe
sYe
sYe
sYe
sYe
sYe
sYe
s
aB
ased
on
Agu
irre
chu
(200
9),u
nle
sssp
ecifi
edo
ther
wis
e.Ty
pe:
1-
Scie
nce
Cit
ies,
“sci
enti
fic
core
”es
tab
lish
edin
exis
tin
gci
ties
,wh
ich
oft
enh
ada
par
ticu
lar
his
tori
cals
ign
ifi-
can
ce;2
-Sci
ence
Cit
ies
that
rece
ived
city
stat
us
sim
ult
aneo
usl
y(o
ra
few
year
sla
ter)
wit
hth
ecr
eati
on
ofa
scie
nti
fic
or
scie
nti
fic-
ind
ust
rial
com
ple
xat
ap
ract
ical
lyn
ewlo
cati
on
(oft
enin
the
“op
enfi
eld
”);3
-Sc
ien
ceC
itie
sth
ath
ave
aris
enin
exis
tin
gse
ttle
men
tsan
dre
ceiv
edci
tyst
atu
saf
ter
ob
tain
ing
scie
nti
fic
fun
ctio
ns;
4-
Scie
nce
Cit
ies
that
do
no
t
hav
eci
tyst
atu
s;5
-ac
adem
icto
wn
.b
Bas
edo
nN
AS
(200
2).
cB
ased
on
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po
and
Poly
an(2
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Ru
ssia
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ikip
edia
arti
cle
on
clo
sed
citi
es(Z
ATO
),28
Sep
tem
ber
2016
.e
Wik
iped
iaar
ticl
esfo
rea
chci
ty,2
8Se
pte
mb
er20
16.
fR
uss
ian
Wik
iped
iaar
ticl
eo
nSc
ien
ceC
itie
s,28
Sep
tem
ber
2016
.
A.1
Tab
leA
.1–
con
tin
ued
fro
mp
revi
ou
sp
age
Nau
kogr
adC
lose
dci
tyd
Pri
ori
tysp
ecia
lisa
tio
nar
easa
No.
Lo
cati
on
aO
bla
st
Foundede
YearSovietstatuse
YearRussianstatusf
Typea
Pas
tN
ow
Militarybase
Airrocketandspace
research
Electronicsandradio
engineering
Automation,ITand
instrumentation
Chemistry,chemicalphysics
andnewmaterials
Nuclearcomplex
Energetics
Biology,biotechnologyand
agriculturalsciences
18Z
elen
ogo
rsk
Kra
snoy
arsk
1956
1956
2Ye
sYe
sN
oN
oN
oN
oYe
sYe
sN
oN
o
19Z
hel
ezn
ogo
rsk
Kra
snoy
arsk
1950
1954
2Ye
sYe
sN
oYe
sN
oN
oN
oYe
sN
oN
o
20K
urc
hat
ovc
Ku
rsk
1968
1976
2N
oN
oN
oN
oN
oN
oN
oYe
sYe
sN
o
21G
atch
ina
Len
ingr
ad19
2819
561
No
No
No
No
No
Yes
No
Yes
No
No
22P
rim
ors
kLe
nin
grad
1268
1948
1N
oN
oN
oYe
sN
oN
oN
oN
oN
oN
o
23So
snov
yB
or
Len
ingr
ad19
5819
623
Yes
Yes
No
No
Yes
No
No
Yes
Yes
No
24Z
elen
ogr
adM
osc
owC
ity
1958
1958
2N
oN
oN
oN
oYe
sYe
sN
oN
oN
oN
o
25A
vto
po
ligo
nM
osc
owO
bla
st19
6419
644
No
No
No
No
No
Yes
No
No
No
No
26B
alas
hik
ha
Mo
scow
Ob
last
1830
1942
1N
oN
oN
oYe
sN
oYe
sN
oN
oN
oN
o
27B
elo
ozer
sky
Mo
scow
Ob
last
1961
1961
4N
oN
oN
oYe
sN
oN
oN
oN
oN
oN
o
28C
her
no
golo
vka
Mo
scow
Ob
last
1710
1956
2008
3N
oN
oN
oN
oN
oYe
sYe
sN
oN
oN
o
29D
olg
op
rud
ny
Mo
scow
Ob
last
1931
1951
2N
oN
oN
oN
oN
oYe
sYe
sN
oN
oN
o
30D
ub
na
Mo
scow
Ob
last
1956
1956
2001
2N
oN
oN
oYe
sN
oYe
sN
oYe
sN
oN
o
31D
zerz
hin
sky
Mo
scow
Ob
last
1938
1956
3N
oN
oN
oYe
sN
oYe
sYe
sN
oN
oN
o
32Fr
yazi
no
Mo
scow
Ob
last
1584
1953
2003
3N
oN
oN
oYe
sYe
sYe
sN
oN
oN
oN
o
33Is
tra
Mo
scow
Ob
last
1589
1946
1N
oN
oN
oYe
sN
oYe
sN
oN
oYe
sN
o
34K
him
kiM
osc
owO
bla
st18
5019
501
No
No
No
Yes
No
Yes
No
No
No
No
35K
lim
ovsk
Mo
scow
Ob
last
1882
1940
1N
oN
oN
oN
oN
oYe
sN
oN
oN
oN
o
36K
oro
lyov
Mo
scow
Ob
last
1938
1946
2001
1N
oN
oN
oYe
sN
oYe
sYe
sN
oN
oN
o
37K
rasn
oar
mey
skM
osc
owO
bla
st19
2819
341
No
No
No
No
No
Yes
Yes
No
No
No
aB
ased
on
Agu
irre
chu
(200
9),u
nle
sssp
ecifi
edo
ther
wis
e.Ty
pe:
1-
Scie
nce
Cit
ies,
“sci
enti
fic
core
”es
tab
lish
edin
exis
tin
gci
ties
,wh
ich
oft
enh
ada
par
ticu
lar
his
tori
cals
ign
ifi-
can
ce;2
-Sci
ence
Cit
ies
that
rece
ived
city
stat
us
sim
ult
aneo
usl
y(o
ra
few
year
sla
ter)
wit
hth
ecr
eati
on
ofa
scie
nti
fic
or
scie
nti
fic-
ind
ust
rial
com
ple
xat
ap
ract
ical
lyn
ewlo
cati
on
(oft
enin
the
“op
enfi
eld
”);3
-Sc
ien
ceC
itie
sth
ath
ave
aris
enin
exis
tin
gse
ttle
men
tsan
dre
ceiv
edci
tyst
atu
saf
ter
ob
tain
ing
scie
nti
fic
fun
ctio
ns;
4-
Scie
nce
Cit
ies
that
do
no
t
hav
eci
tyst
atu
s;5
-ac
adem
icto
wn
.b
Bas
edo
nN
AS
(200
2).
cB
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edia
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cle
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sed
citi
es(Z
ATO
),28
Sep
tem
ber
2016
.e
Wik
iped
iaar
ticl
esfo
rea
chci
ty,2
8Se
pte
mb
er20
16.
fR
uss
ian
Wik
iped
iaar
ticl
eo
nSc
ien
ceC
itie
s,28
Sep
tem
ber
2016
.
A.2
Tab
leA
.1–
con
tin
ued
fro
mp
revi
ou
sp
age
Nau
kogr
adC
lose
dci
tyd
Pri
ori
tysp
ecia
lisa
tio
nar
easa
No.
Lo
cati
on
aO
bla
st
Foundede
YearSovietstatuse
YearRussianstatusf
Typea
Pas
tN
ow
Militarybase
Airrocketandspace
research
Electronicsandradio
engineering
Automation,ITand
instrumentation
Chemistry,chemicalphysics
andnewmaterials
Nuclearcomplex
Energetics
Biology,biotechnologyand
agriculturalsciences
38K
rasn
ogo
rskc
Mo
scow
Ob
last
1932
1942
3N
oN
oN
oN
oYe
sN
oN
oN
oN
oN
o
39K
rasn
ozn
amen
skM
osc
owO
bla
st19
5019
502
Yes
Yes
No
Yes
No
No
No
No
No
No
40Lu
khov
itsy
Mo
scow
Ob
last
1594
1957
?1
No
No
No
Yes
No
No
No
No
No
No
41Ly
tkar
ino
Mo
scow
Ob
last
1939
1957
1N
oN
oN
oYe
sN
oYe
sN
oN
oN
oN
o
42Ly
ub
erts
icM
osc
owO
bla
st16
2319
481
No
No
No
No
No
Yes
No
No
No
No
43M
end
eley
evo
Mo
scow
Ob
last
1957
1965
4N
oN
oN
oN
oN
oYe
sN
oN
oN
oN
o
44M
ytis
hch
icM
osc
owO
bla
st14
6019
351
No
No
No
No
No
Yes
No
No
No
No
45O
bo
len
skM
osc
owO
bla
st19
7519
754
Yes
No
No
No
No
No
No
No
No
Yes
46O
revo
Mo
scow
Ob
last
1954
1954
4N
oN
oN
oYe
sN
oN
oN
oN
oN
oN
o
47Pe
resv
etM
osc
owO
bla
st19
4819
482
No
No
No
Yes
No
No
No
No
No
No
48P
rotv
ino
Mo
scow
Ob
last
1960
1960
2008
3Ye
sN
oN
oN
oN
oYe
sN
oYe
sN
oN
o
49P
ush
chin
oM
osc
owO
bla
st19
5619
6620
053
No
No
No
No
No
No
No
No
No
Yes
50R
emm
ash
Mo
scow
Ob
last
1957
1957
4N
oN
oN
oYe
sN
oN
oN
oN
oN
oN
o
51R
euto
vM
osc
owO
bla
st14
92-1
495
1940
2003
1N
oN
oN
oYe
sN
oYe
sN
oN
oN
oN
o
52To
mil
ino
Mo
scow
Ob
last
1894
1961
4N
oN
oN
oYe
sN
oYe
sN
oN
oN
oN
o
53Tr
oit
skM
osc
owO
bla
st16
1719
7720
073
No
No
No
No
No
Yes
No
Yes
No
No
54Yu
bile
yny
Mo
scow
Ob
last
1939
1950
3Ye
sN
oN
oYe
sN
oN
oN
oN
oN
oN
o
55Z
hel
ezn
od
oro
zhn
yM
osc
owO
bla
st18
6119
523
No
No
No
No
No
Yes
No
No
No
No
56Z
hu
kovs
kyM
osc
owO
bla
st19
3319
4720
072
No
No
No
Yes
No
No
No
No
No
No
57Z
vyoz
dn
ygo
rod
ok
Mo
scow
Ob
last
1960
1960
4Ye
sYe
sN
oYe
sN
oN
oN
oN
oN
oN
o
aB
ased
on
Agu
irre
chu
(200
9),u
nle
sssp
ecifi
edo
ther
wis
e.Ty
pe:
1-
Scie
nce
Cit
ies,
“sci
enti
fic
core
”es
tab
lish
edin
exis
tin
gci
ties
,wh
ich
oft
enh
ada
par
ticu
lar
his
tori
cals
ign
ifi-
can
ce;2
-Sci
ence
Cit
ies
that
rece
ived
city
stat
us
sim
ult
aneo
usl
y(o
ra
few
year
sla
ter)
wit
hth
ecr
eati
on
ofa
scie
nti
fic
or
scie
nti
fic-
ind
ust
rial
com
ple
xat
ap
ract
ical
lyn
ewlo
cati
on
(oft
enin
the
“op
enfi
eld
”);3
-Sc
ien
ceC
itie
sth
ath
ave
aris
enin
exis
tin
gse
ttle
men
tsan
dre
ceiv
edci
tyst
atu
saf
ter
ob
tain
ing
scie
nti
fic
fun
ctio
ns;
4-
Scie
nce
Cit
ies
that
do
no
t
hav
eci
tyst
atu
s;5
-ac
adem
icto
wn
.b
Bas
edo
nN
AS
(200
2).
cB
ased
on
Lap
po
and
Poly
an(2
008)
.d
Ru
ssia
nW
ikip
edia
arti
cle
on
clo
sed
citi
es(Z
ATO
),28
Sep
tem
ber
2016
.e
Wik
iped
iaar
ticl
esfo
rea
chci
ty,2
8Se
pte
mb
er20
16.
fR
uss
ian
Wik
iped
iaar
ticl
eo
nSc
ien
ceC
itie
s,28
Sep
tem
ber
2016
.
A.3
Tab
leA
.1–
con
tin
ued
fro
mp
revi
ou
sp
age
Nau
kogr
adC
lose
dci
tyd
Pri
ori
tysp
ecia
lisa
tio
nar
easa
No.
Lo
cati
on
aO
bla
st
Foundede
YearSovietstatuse
YearRussianstatusf
Typea
Pas
tN
ow
Militarybase
Airrocketandspace
research
Electronicsandradio
engineering
Automation,ITand
instrumentation
Chemistry,chemicalphysics
andnewmaterials
Nuclearcomplex
Energetics
Biology,biotechnologyand
agriculturalsciences
58A
pat
ity
(Aka
dem
goro
do
k)M
urm
ansk
1926
1954
2N
oN
oN
oYe
sYe
sYe
sYe
sYe
sYe
sYe
s
59Po
lyar
nye
Zo
ric
Mu
rman
sk19
6819
732
No
No
No
No
No
No
No
Yes
Yes
No
60B
alak
hn
a(P
ravd
insk
)N
izh
ny
Nov
goro
d19
3219
411
No
No
No
No
Yes
No
No
No
No
No
61D
zerz
hin
skN
izh
ny
Nov
goro
d16
0619
302
No
No
No
No
No
No
Yes
No
No
No
62Sa
rov
Niz
hn
yN
ovgo
rod
1310
1947
1Ye
sYe
sN
oN
oN
oN
oN
oYe
sN
oN
o
63A
kad
emgo
rod
ok
Nov
osi
bir
sk19
5719
575
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
64K
olt
sovo
Nov
osi
bir
sk19
7919
7920
034
Yes
No
No
No
No
No
No
No
No
Yes
65K
rasn
oo
bsk
Nov
osi
bir
sk19
7019
784
No
No
No
No
No
No
No
No
No
Yes
66N
ovo
sib
irsk
-49b
Nov
osi
bir
skN
oN
oN
oN
oN
oN
oN
oN
oN
o
67O
msk
-5b
Om
skN
oN
o
68Z
arec
hn
yPe
nza
1954
1958
2Ye
sYe
sN
oN
oN
oN
oN
oYe
sYe
sN
o
69Pe
rm-6
bPe
rmN
oN
o
70B
ols
hoy
Kam
enc
Pri
mo
rsk
Kra
i19
4719
542
Yes
Yes
No
No
No
Yes
No
No
No
No
71Vo
lgo
do
nsk
cR
ost
ov19
5019
763
No
No
No
No
No
No
No
Yes
Yes
No
72Z
ern
ogr
adR
ost
ov19
2919
351
No
No
Yes
No
No
No
No
No
No
Yes
73Pe
terg
of
Sain
tPet
ersb
urg
1711
1960
2005
1N
oN
oN
oN
oN
oYe
sYe
sN
oN
oN
o
74D
esn
ogo
rskc
Smo
len
sk19
7419
822
No
No
No
No
No
No
No
Yes
Yes
No
75Le
snoy
Sver
dlo
vsk
1947
1954
2Ye
sYe
sN
oN
oN
oN
oN
oYe
sN
oN
o
76N
izh
nya
yaSa
lda
Sver
dlo
vsk
1760
1958
1N
oN
oN
oYe
sN
oN
oN
oN
oN
oN
o
77N
ovo
ura
lsk
Sver
dlo
vsk
1941
1949
2Ye
sYe
sN
oN
oN
oN
oYe
sYe
sN
oN
o
aB
ased
on
Agu
irre
chu
(200
9),u
nle
sssp
ecifi
edo
ther
wis
e.Ty
pe:
1-
Scie
nce
Cit
ies,
“sci
enti
fic
core
”es
tab
lish
edin
exis
tin
gci
ties
,wh
ich
oft
enh
ada
par
ticu
lar
his
tori
cals
ign
ifi-
can
ce;2
-Sci
ence
Cit
ies
that
rece
ived
city
stat
us
sim
ult
aneo
usl
y(o
ra
few
year
sla
ter)
wit
hth
ecr
eati
on
ofa
scie
nti
fic
or
scie
nti
fic-
ind
ust
rial
com
ple
xat
ap
ract
ical
lyn
ewlo
cati
on
(oft
enin
the
“op
enfi
eld
”);3
-Sc
ien
ceC
itie
sth
ath
ave
aris
enin
exis
tin
gse
ttle
men
tsan
dre
ceiv
edci
tyst
atu
saf
ter
ob
tain
ing
scie
nti
fic
fun
ctio
ns;
4-
Scie
nce
Cit
ies
that
do
no
t
hav
eci
tyst
atu
s;5
-ac
adem
icto
wn
.b
Bas
edo
nN
AS
(200
2).
cB
ased
on
Lap
po
and
Poly
an(2
008)
.d
Ru
ssia
nW
ikip
edia
arti
cle
on
clo
sed
citi
es(Z
ATO
),28
Sep
tem
ber
2016
.e
Wik
iped
iaar
ticl
esfo
rea
chci
ty,2
8Se
pte
mb
er20
16.
fR
uss
ian
Wik
iped
iaar
ticl
eo
nSc
ien
ceC
itie
s,28
Sep
tem
ber
2016
.
A.4
Tab
leA
.1–
con
tin
ued
fro
mp
revi
ou
sp
age
Nau
kogr
adC
lose
dci
tyd
Pri
ori
tysp
ecia
lisa
tio
nar
easa
No.
Lo
cati
on
aO
bla
st
Foundede
YearSovietstatuse
YearRussianstatusf
Typea
Pas
tN
ow
Militarybase
Airrocketandspace
research
Electronicsandradio
engineering
Automation,ITand
instrumentation
Chemistry,chemicalphysics
andnewmaterials
Nuclearcomplex
Energetics
Biology,biotechnologyand
agriculturalsciences
78Ve
rhn
yaya
Sald
acSv
erd
lovs
k17
7819
333
No
No
No
No
No
No
Yes
No
No
No
79Z
arec
hn
ySv
erd
lovs
k19
5519
552
No
No
No
No
No
No
No
Yes
Yes
No
80M
ich
uri
nsk
Tam
bov
1635
1932
2003
1N
oN
oN
oN
oN
oYe
sN
oN
oN
oYe
s
81Z
elen
od
ols
kcTa
tars
tan
1865
1949
1N
oN
oN
oN
oN
oYe
sN
oN
oN
oN
o
82A
kad
emgo
rod
ok
Tom
sk19
7219
725
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
83Se
vers
kTo
msk
1949
1949
2Ye
sYe
sN
oN
oN
oN
oYe
sYe
sN
oN
o
84R
edki
no
Tve
r18
4319
504
No
No
No
No
Yes
No
Yes
No
No
No
85So
lnec
hn
yT
ver
1947
1951
4Ye
sYe
sYe
sN
oN
oYe
sN
oN
oN
oN
o
86U
do
mly
acT
ver
1478
1984
3N
oN
oN
oN
oN
oN
oN
oYe
sYe
sN
o
87G
lazo
vcU
dm
urt
ia16
7819
481
No
No
No
No
No
No
No
Yes
No
No
88Vo
tkin
skc
Ud
mu
rtia
1759
1957
1N
oN
oN
oYe
sN
oYe
sN
oN
oN
oN
o
89D
imit
rovg
rad
Uly
anov
sk16
9819
561
No
No
No
No
No
No
No
Yes
Yes
No
90K
ovro
vV
lad
imir
1778
1916
1N
oN
oN
oN
oN
oYe
sN
oN
oN
oN
o
91M
elen
kiV
lad
imir
1778
1N
oN
oN
oN
oN
oYe
sN
oN
oN
oN
o
92R
adu
zhn
yV
lad
imir
1971
1971
2Ye
sYe
sN
oN
oYe
sYe
sN
oN
oN
oN
o
93N
ovov
oro
nez
hc
Voro
nez
h19
5719
642
No
No
No
No
No
No
No
Yes
Yes
No
94B
oro
kYa
rosl
avl
1807
1956
4N
oN
oN
oN
oN
oN
oN
oN
oN
oYe
s
95Pe
resl
avl-
Zal
essk
yYa
rosl
avl
1152
1964
1N
oN
oN
oN
oN
oYe
sYe
sN
oN
oN
o
aB
ased
on
Agu
irre
chu
(200
9),u
nle
sssp
ecifi
edo
ther
wis
e.Ty
pe:
1-
Scie
nce
Cit
ies,
“sci
enti
fic
core
”es
tab
lish
edin
exis
tin
gci
ties
,wh
ich
oft
enh
ada
par
ticu
lar
his
tori
cals
ign
ifi-
can
ce;2
-Sci
ence
Cit
ies
that
rece
ived
city
stat
us
sim
ult
aneo
usl
y(o
ra
few
year
sla
ter)
wit
hth
ecr
eati
on
ofa
scie
nti
fic
or
scie
nti
fic-
ind
ust
rial
com
ple
xat
ap
ract
ical
lyn
ewlo
cati
on
(oft
enin
the
“op
enfi
eld
”);3
-Sc
ien
ceC
itie
sth
ath
ave
aris
enin
exis
tin
gse
ttle
men
tsan
dre
ceiv
edci
tyst
atu
saf
ter
ob
tain
ing
scie
nti
fic
fun
ctio
ns;
4-
Scie
nce
Cit
ies
that
do
no
t
hav
eci
tyst
atu
s;5
-ac
adem
icto
wn
.b
Bas
edo
nN
AS
(200
2).
cB
ased
on
Lap
po
and
Poly
an(2
008)
.d
Ru
ssia
nW
ikip
edia
arti
cle
on
clo
sed
citi
es(Z
ATO
),28
Sep
tem
ber
2016
.e
Wik
iped
iaar
ticl
esfo
rea
chci
ty,2
8Se
pte
mb
er20
16.
fR
uss
ian
Wik
iped
iaar
ticl
eo
nSc
ien
ceC
itie
s,28
Sep
tem
ber
2016
.
A.5
BM
un
icip
alle
veld
ata
sou
rces
and
vari
able
s
Tab
leB
.1:M
un
icip
alle
veld
ata
sou
rces
and
vari
able
s
Dat
aty
pe
Dat
asu
b-t
ype
Dat
aso
urc
eD
escr
ipti
on
Fact
ors
guid
ing
the
sele
ctio
no
flo
cati
on
ofs
cien
ceci
ties
Ad
min
istr
ativ
eV
ario
us
iden
tifi
cati
on
info
rmat
ion
for
mu
nic
ipal
ity,
regi
on
and
fed
-
eral
dis
tric
t
Op
enSt
reet
Map
s,av
aila
ble
thro
ugh
GIS
-
LA
B(http://gis-lab.info/qa/osm-
adm.html
)
Un
iqu
em
un
icip
alit
y,fe
der
ald
istr
ict
and
regi
on
(ob
last
,kr
ai,
rep
ub
lic)
iden
tifi
cato
rs,
cod
esan
d
nam
es
Pop
ula
tio
n19
59ce
nsu
sd
ata
Jan
uar
y19
59So
viet
Cen
sus,
avai
l-
able
thro
ugh
Dem
osc
op
e(http:
//demoscope.ru/weekly/ssp/census.
php?cy=3
)
All
po
pu
lati
on
inm
un
icip
alit
yin
1959
,est
imat
esfo
r
som
em
un
icip
alit
ies
Geo
grap
hy
Are
aC
alcu
late
din
QG
ISb
ased
on
Op
enSt
reet
Map
s
Mu
nic
ipal
ity
area
calc
ula
ted
inQ
GIS
,m
easu
red
in
squ
ared
kilo
met
ers
Co
ord
inat
eso
fth
em
un
icip
alit
y
cen
tre
GP
Sco
ord
inat
eso
fth
ece
ntr
eo
fmu
nic
ipal
ity
calc
u-
late
din
QG
IS
Alt
itu
de
CG
IAR
.aC
on
sort
ium
for
Spat
ial
Info
rma-
tio
n(C
GIA
R-C
SI)
SRT
M90
mD
igit
alE
leva
-
tio
nD
ata,
vers
ion
4,av
aila
ble
athttp://
srtm.csi.cgiar.org/
Alt
itu
de
of
mu
nic
ipal
ity
inm
eter
s(m
ean
,m
edia
n,
SD,m
inan
dm
axva
lue)
Tem
per
atu
res
inJa
nu
ary
and
July
Wo
rld
Cli
mve
rsio
n1
(http://www.
worldclim.org/version1
),d
evel
op
ed
by
Hij
man
set
al.(
2005
)
Mo
nth
lyte
mp
erat
ure
dat
a,fo
rth
ep
erio
d19
60-
1990
,as
sign
edto
mu
nic
ipal
itie
sin
QG
IS.
Ave
rage
,
med
ian
,st
and
ard
dev
iati
on
,m
inim
um
,an
dm
axi-
mu
m.
Co
nti
nu
edo
nn
extp
age
A.6
Tab
leB
.1–
Co
nti
nu
edfr
om
pre
vio
us
pag
e
Dat
aty
pe
Dat
asu
b-t
ype
Dat
aso
urc
eD
escr
ipti
on
Rai
lro
adVe
rnad
sky
Stat
eG
eolo
gica
lM
use
um
and
U.S
.G
eolo
gica
lSu
rvey
,20
0106
00(2
001)
and
Cen
tral
man
agem
ent
un
ito
fth
em
il-
itar
yco
mm
un
icat
ion
so
fth
eR
edA
rmy
(194
3)
Dat
ao
nra
ilro
ads
wer
eco
nst
ruct
edu
sin
gra
ilro
ads
shap
efile
des
crib
ing
the
rail
road
so
fth
efo
rmer
So-
viet
Un
ion
aso
fth
eea
rly
1990
sp
rep
ared
by
Ver-
nad
sky
Stat
eG
eolo
gica
lM
use
um
and
U.S
.Geo
log-
ical
Surv
ey,
2001
0600
(200
1),
alo
ng
wit
ha
map
of
rail
road
sfr
om
1943
fro
mC
entr
alm
anag
emen
tu
nit
of
the
mil
itar
yco
mm
un
icat
ion
so
fth
eR
edA
rmy
(194
3)to
man
ual
lyre
mov
ean
yd
iffe
ren
ces
bet
wee
n
the
situ
atio
nd
epic
ted
inth
esh
apefi
lean
dth
e19
43
map
.In
dic
ato
req
ual
to1
ifm
un
icip
alit
yh
asac
cess
tora
ilro
ad,a
nd
0o
ther
wis
e.
Co
astl
ine/
maj
or
rive
r/la
keN
atu
ral
Ear
th,
1:10
mP
hys
ical
Vect
ors
ver-
sio
n(http://www.naturalearthdata.
com/downloads/10m-physical-
vectors/
)
Ind
icat
or
equ
alto
1if
mu
nic
ipal
ity
has
acce
ssto
coas
t/m
ajo
rri
ver/
lake
and
0o
ther
wis
e.
Dis
tan
ces
Cal
cula
ted
inQ
GIS
bas
edo
nth
eso
urc
es
spec
ified
abov
e
Dis
tan
ce(i
nkm
)fr
om
the
cen
tre
of
mu
nic
ipal
ity
to
the
nea
rest
railr
oad
,co
ast,
maj
or
rive
r,la
ke,
USS
R
bo
rder
,pla
nt(
ofa
ny
typ
e),a
nd
HE
I(o
fan
yty
pe)
.
Leve
lo
fin
du
stri
ald
evel
-
op
men
t
Dat
ao
nth
efa
cto
ries
,res
earc
han
d
des
ign
esta
blis
hm
ents
of
the
So-
viet
def
ence
ind
ust
ryin
1947
Dex
ter
and
Ro
dio
nov
(201
6).
Th
ed
atas
et
con
tain
sal
mo
st30
,000
entr
ies
and
in-
clu
des
the
nam
e,lo
cati
on
,mai
nb
ran
cho
f
def
ence
pro
du
ctio
n,e
stab
lish
men
tty
pe
as
wel
las
the
star
tan
den
dd
ate
for
the
esta
b-
lish
men
t’sm
ilita
ryw
ork
.
Nu
mb
ero
fall
pla
nts
,reg
ard
less
oft
ype
Hig
her
edu
cati
on
inst
i-
tute
s(H
EI)
HE
Isin
the
mu
nic
ipal
ity
in19
59D
eW
itt(
1961
)N
um
ber
of
all
HE
Is,
HE
Issp
ecia
lisn
gin
tech
nic
al
scie
nce
s,an
dH
EIs
spec
iali
sin
gin
bio
logy
and
med
-
ical
scie
nce
s
Co
nti
nu
edo
nn
extp
age
A.7
Tab
leB
.1–
Co
nti
nu
edfr
om
pre
vio
us
pag
e
Dat
aty
pe
Dat
asu
b-t
ype
Dat
aso
urc
eD
escr
ipti
on
R&
Din
stit
ute
sR
&D
inst
itu
tes
aso
f195
9V
ario
us
op
enso
urc
es(p
rim
arily
Wik
iped
ia)
Alo
wer
bo
un
dfo
rth
en
um
ber
of
R&
Din
stit
ute
s
(RA
S,n
on
-RA
San
dto
tal)
ho
sted
inm
un
icip
alit
yin
1959
-in
clu
des
on
lyth
ose
esta
bli
shed
bef
ore
or
in
1959
and
stil
lexi
stin
gn
ow.
RA
S-
Ru
ssia
nA
cad
emy
ofS
cien
ce.
Lo
ng-
term
ou
tco
mes
ofi
nte
rest
Pate
nts
Ap
plic
atio
ns
toE
PO
Eu
rop
ean
Pate
ntO
ffice
.Pat
ents
are
mat
ched
tom
un
icip
alit
ies
via
inve
nto
rs’
add
ress
es.
Nu
mb
ero
fp
aten
tsap
pli
cati
on
sto
EP
Oin
2006
-
2015
,by
inve
nto
r(s
imp
lean
dfr
acti
on
alco
un
tin
g)
Pop
ula
tio
n20
10ce
nsu
sd
ata
2010
Ru
ssia
nC
ensu
s,av
aila
ble
at
http://www.gks.ru/free_doc/new_
site/perepis2010/croc/perepis_
itogi1612.htm
All
po
pu
lati
on
,p
op
ula
tio
nw
ith
hig
her
edu
cati
on
,
and
po
pu
lati
on
wit
hP
hD
ord
oct
ora
ldeg
rees
inm
u-
nic
ipal
ity
in20
10
Nig
htt
ime
ligh
tsA
vera
gest
able
nig
htl
igh
tsVe
rsio
n4
DM
SP-O
LSN
igh
ttim
eLi
ghts
Tim
eSe
ries
,N
atio
nal
Oce
anic
and
Ato
msp
her
icA
dm
inis
trat
ion
(NO
AA
)
(http://ngdc.noaa.gov/eog/dmsp/
downloadV4composites.html
)
Ave
rage
nig
htl
igh
tsfo
r19
92-1
994
and
2009
-201
1,
clea
ned
ofg
asfl
ares
SME
sR
esu
lts
oft
he
2010
SME
cen
sus
Ro
ssta
t(F
eder
alSt
ate
Stat
isti
csSe
r-
vice
)(http://www.gks.ru/free_doc/
new_site/business/prom/small_
business/itog-spn.html
)
Th
ed
atas
etco
nta
ins
info
rmat
ion
on
the
nu
mb
er
of
firm
s,re
ven
ue,
nu
mb
ero
fem
plo
yees
,fi
xed
as-
sets
,an
dfi
xed
cap
ital
inve
stm
ent
by
size
,1-
and
4-
dig
itIS
ICse
cto
r.W
eu
seSM
Es
per
cap
ita,
SME
sale
s
per
wo
rker
(lab
ou
rp
rod
uct
ivit
y),a
nd
SME
sale
sp
er
un
ito
ffi
xed
lab
ou
r,al
lse
cto
rsan
dm
anu
fact
uri
ng
on
ly.
Th
eSM
Ece
nsu
sd
oes
no
tco
ver
ZAT
Os,
so16
scie
nce
citi
es,
wh
ich
are
also
ZAT
Os,
are
no
tco
v-
ered
.
aC
GIA
Ris
agl
ob
alp
artn
ersh
ipo
fres
earc
ho
rgan
isat
ion
sd
edic
ated
tore
du
cin
gp
over
tyan
dh
un
ger,
imp
rovi
ng
hu
man
hea
lth
and
nu
trit
ion
,an
den
han
cin
gec
on
-
syst
emre
sili
ence
thro
ugh
agri
cult
ura
lres
earc
h.
CG
IAR
-CSI
issp
atia
lsci
ence
com
mu
nit
yth
atfa
cili
tate
sC
GIA
R’s
inte
rnat
ion
alag
ricu
ltu
rald
evel
op
men
tre
sear
ch
usi
ng
spat
iala
nal
ysis
,GIS
,an
dre
mo
tese
nsi
ng:http://www.cgiar-csi.org/
.
A.8
C BEEPS V Russia
BEEPS is an enterprise survey whose objective is to gain an understanding of firms’ per-
ception of the environment in which they operate in order to be able to assess the con-
straints to private sector growth and enterprise performance. It covers topics related to
infrastructure, sales and supplies, degree of competition, land and permits, crime, fi-
nance, business-government relations, labour and establishment performance. BEEPS
is implemented by private contractors, using face-to-face interviews in the country’s of-
ficial language(s).
In BEEPS V, 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 Ta-
ble C.1).
Table C.1: BEEPS V Russia sample breakdown
Region Number of interviews Region Number of interviewsCentral 1124 Siberian 709Belgorod 120 Irkutsk 131Kaluga 121 Kemerovo 124Kursk 87 Krasnoyarsk 89Lipetsk 121 Novosibirsk 123Moscow City 121 Omsk 120Moscow Oblast 122 Tomsk 122
Smolensk 71 Southern 328Tver 120 Krasnodar 88Voronezh 121 Rostov 120Yaroslavl 120 Volgograd 120Far Eastern 334 Urals 199Khabarovsk 122 Chelyabinsk 79Primorsky Krai 120 Sverdlovsk 120
Sakha (Yakutia) 92 Volga 922
North Caucasian 120 Bashkortostan 106Stavropol Krai 120 Kirov 134
Northwestern 484 Mordovia 120Kaliningrad 122 Nizhni Novgorod 82Leningrad 120 Perm 120Murmansk 120 Samara 120St. Petersburg 122 Tatarstan 120
Ulyanovsk 120Total 4220
Source: EBRD-World Bank BEEPS V Russia.
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
A.9
process, organisation and marketing innovation, as well as R&D and protection of in-
novation. 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 OECD and Statistical Office of the European Communities (2005). The so-called
filtering questions were asked with the help of show cards, which contained examples
of the relevant innovations to facilitate a common understanding of the definition of
innovation. While non-innovators did not receive additional questions on innovations,
innovating firms were asked to provide more information, including a detailed descrip-
tion of their main product or process innovation (in terms of impact on sales or costs
respectively).
Firms were only asked the relevant parts of the Innovation Module, which in turn col-
lected 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.
Firms were asked to specify their main innovative product and process. More than 90
per cent of Innovation Module interviews were completed face-to-face immediately af-
ter 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. The interviews lasted on average 18.5 minutes.
The detailed descriptions of the firms’ main product or process innovation were used
to analyze whether the respective innovation complies with the formal definitions of
product and process innovation, taking into account the firm’s main business. Based on
this assessment, innovators may be reclassified as non-innovators, or moved to another
category of innovation than the one self-reported. As a result, about two thirds of the
self-reported innovations were reclassified, whereby 24 per cent were no longer classi-
fied as innovating firm, while the remaining innovations were reclassified according to
their type. The cleaning of innovations can only be done for product or process, i.e.
technological innovations, as no additional questions were asked for non-technological
innovations.
In Russia, only 51.9 per cent of companies that said they 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 mod-
ule. There was a significant variation across regions on all of these measures, which
could reflect both the competence of interviewers as well as understanding of the re-
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spondents. We are not able to do the same for organizational and marketing innovation,
since we did not ask the respondents to name the main organisational and marketing
innovations.
D An Outline of the Soviet Innovation System
Throughout its history, the Soviet Union paid significant attention to R&D. Employment
in this sector grew from 35,000 in 1922 to more than 2.8 million in 1990, with intensive
investment made in R&D facilities and equipment along the way (Gokhberg, 1997). R&D
expenditures were also increasing in absolute terms until 1990, when they accounted for
about 3.5 per cent of GNP (Gokhberg and Mindely, 1993).
Despite these efforts, the specific characteristics of the Soviet economic and polit-
ical system harmed the efficiency of the R&D sector. In the absence of market forces,
R&D plans and findings were set as a result of bargaining among various parties in the
government and the R&D sector while the prices of R&D products were based on their
estimated costs. Following the branch-based structure of production and widespread
concerns for secrecy, the Soviet scientific sector was dominated by large multipurpose
R&D institutions able to exercise monopolistic power in the relevant fields (Schneider,
1994). General policies of autarky implemented by the Soviet Union were also applied to
science, resulting in the low level of technology and ideas exchange between the country
and the international community (Gokhberg, 1997).
These factors led to various inefficiencies in the sector. Deep governmental involve-
ment biased the concentration of Soviet R&D towards politically important areas such as
defence and engineering at the expense of fundamental research such as medical and
life sciences (Gokhberg and Mindely, 1993). Being subject to very specific incentives,
R&D efforts were often focused on obsolete areas and produced low-quality results. In
the struggle for funding, R&D institutions were reluctant to share their technologies and
were artificially increasing their staff by keeping older cohorts of researchers in employ-
ment.31 As a result of this disproportional increase in R&D personnel, increases in fund-
ing were mainly allocated to wages rather than to buying new state-of-the-art equip-
ment (Schneider, 1994). On the top of these problems, low incentives to innovate in the
production sector and weak diffusion of innovations across enterprises and industries
31For example, in the late 1980s almost 80 per cent of Soviet researchers were aged 50 years or more,compared to 34 per cent in the United States (Schneider, 1994).
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hampered the utilization of the suboptimal output of the Soviet R&D.
R&D was largely separated from higher education, with universities becoming al-
most exclusively training centers. Basic research was instead concentrated in the sys-
tem of Academy of Sciences and branch academies of agricultural sciences, medical sci-
ences, and education. Applied R&D, on the other hand, was concentrated in the indus-
trial R&D units, which were established by each branch ministry. Enterprise R&D was
the least developed, as enterprises had no incentives to introduce new products and
processes. Hence, even in cases where the Soviet Union had a leading position in the
development of significant innovations, it fell behind others in diffusion of innovations.
These peculiar characteristics of Soviet R&D have to be taken into account while
analysing the development of the sector during the transition period. Quoting Gokhberg
(1997), “[o]nly a part of the R&D sector inherited from the Soviet era can and should be
preserved” (p. 9). This view was arguably shared by market reformers in the 1990’s, when
the Russian R&D sector went through a painful adjustment as part of Russia’s transfor-
mation into a market economy.
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E Matching: Graphs and Additional Statistics
Figure E.1: Matching for non-SME oucomes; distribution of the p-score (scatter plots). Topgraph: distribution before matching. Bottom graph: distribution after matching.
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Figure E.2: Matching for SME oucomes; distribution of the p-score (scatter plots). Top graph:distribution before matching. Bottom graph: distribution after matching.
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Figure E.3: Matching for non-SME oucomes; distribution of the p-score (kernel densities). Topgraph: estimates before matching. Bottom graph: estimates after matching.
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Figure E.4: Matching for SME oucomes; distribution of the p-score (kernel densities). Top graph:estimates before matching. Bottom graph: estimates after matching.
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Table E.1: Covariate Balance: Mahalanobis Matching, all Science Cities
Matched sample: non-SME outcomesVariable Treated Control Std. bias t-stat. p-value V-ratioLatitude 55.664 55.778 -3.0 -0.20 0.843 0.90Longitude 49.771 49.506 1.2 0.08 0.936 1.13Average temperature in January (°C) -11.632 -11.875 6.6 0.43 0.667 1.21Average temperature in July (°C) 18.535 18.427 6.4 0.42 0.673 1.08Average altitude 0.1686 0.1829 -13.6 -0.90 0.372 0.71Minimum distance from coast 0.7246 0.7565 -7.7 -0.51 0.612 1.04Minimum distance from USSR border 0.6651 0.7034 -10.9 -0.72 0.475 0.92(Log) Population in 1959 2.8761 3.2188 -16.6 -1.09 0.276 1.11(Log) Number of universities in 1959 0.1910 0.1874 0.7 0.05 0.964 1.06(Log) Number of plants in 1947 1.1438 1.1514 -0.6 -0.04 0.967 1.00(Log) Number of R&D institutes in 1959 0.2899 0.2369 8.9 0.59 0.558 1.17(Log) Area in km2 -1.6808 -1.3622 -15.8 -1.04 0.301 0.84
Matched Sample: SME OutcomesVariable Treated Control Std. Bias t-stat. p-value V-ratioLatitude 55.664 56.087 -11.6 -0.70 0.483 1.05Longitude 49.771 46.498 16.0 0.95 0.342 1.49Average temperature in January (°C) -11.632 -11.556 -2.2 -0.13 0.897 1.49Average temperature in July (°C) 18.535 18.365 10.2 0.62 0.539 1.09Average altitude 0.1686 0.1689 -0.3 -0.02 0.987 0.94Minimum distance from coast 0.7246 0.7357 -2.8 -0.17 0.865 1.32Minimum distance from USSR border 0.6651 0.7057 -11.7 -0.71 0.479 1.01(Log) Population in 1959 2.8761 3.6303 -39.0 -2.32 0.022 1.63(Log) Number of universities in 1959 0.1910 0.2123 -4.0 -0.24 0.810 0.91(Log) Number of plants in 1947 1.1438 1.3668 -17.5 -1.07 0.287 0.85(Log) Number of R&D institutes in 1959 0.2899 0.3261 -5.7 -0.35 0.730 0.88(Log) Area in km2 -1.6808 -1.1679 -26.6 -1.62 0.108 0.94
Notes: For each variable, the table reports its mean for both the treated and control groups of the matchedsample, along with the corresponding standardised bias (the treated-control difference as a percentage ofthe combined sample’s standard deviation). In addition, for each variable the table reports the t-statisticand the associated p-value for the difference in the two means, as well as the ratio between the variancesof the treated and the variance of the control group for the variable in question. Both matched samplesare obtained via 1:1 Mahalanobis matching (with replacement), forcing exact matching on the followingdummy variables: presence of a major river, presence of a lake and presence of “historical” railroads in amunicipality.
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Table E.2: Covariate Balance: Propensity Score Matching, Science Cities with no “official” statusin today’s Russia
Matched sample: non-SME outcomesVariable Treated Control Std. bias t-stat. p-value V-ratioLatitude 55.703 55.582 2.8 0.17 0.869 0.72Longitude 50.879 48.097 12.3 0.73 0.467 0.99Average temperature in January (°C) -11.888 -11.507 -7.3 -0.44 0.661 0.41Average temperature in July (°C) 18.516 18.609 -4.6 -0.28 0.784 0.69Average altitude 0.1725 0.1693 2.4 0.14 0.888 0.40Minimum distance from railroad 0.0071 0.0361 -29.1 -1.77 0.079 0.02Minimum distance from river 0.0336 0.0336 0.0 0.00 0.998 1.33Minimum distance from lake 0.1148 0.1249 -9.8 -0.59 0.559 0.51Minimum distance from coast 0.7237 0.6257 23.7 1.41 0.161 0.94Minimum distance from USSR border 0.6765 0.6451 8.3 0.49 0.622 0.90Population in 1959 74.326 64.129 9.2 0.55 0.586 1.92Number of universities in 1959 0.6216 0.3823 12.8 0.76 0.451 2.74Number of plants in 1947 6.9459 5.2206 13.4 0.79 0.430 1.82Number of R&D institutes in 1959 0.9054 0.4707 18.7 1.11 0.270 1.31Area in km2 0.8135 12.890 -18.6 -1.13 0.259 0.02
Matched Sample: SME OutcomesVariable Treated Control Std. Bias t-stat. p-value V-ratioLatitude 55.703 55.708 -0.1 -0.01 0.994 1.12Longitude 50.879 51.148 -1.1 -0.06 0.951 1.20Average temperature in January (°C) -11.888 -12.233 7.5 0.43 0.671 1.15Average temperature in July (°C) 18.516 18.515 0.1 0.00 0.998 1.14Average altitude 0.1725 0.1761 -2.9 -0.16 0.871 0.50Minimum distance from railroad 0.0071 0.0566 -24.7 -1.50 0.135 0.24Minimum distance from river 0.0336 0.0397 -17.0 -0.93 0.354 1.14Minimum distance from lake 0.1148 0.1188 -4.7 -0.26 0.796 0.95Minimum distance from coast 0.7237 0.7136 2.5 0.14 0.892 1.21Minimum distance from USSR border 0.6765 0.6892 -3.5 -0.19 0.846 1.07Population in 1959 74.326 76.104 -1.6 -0.09 0.931 1.95Number of universities in 1959 0.6216 0.6346 -0.6 -0.03 0.973 1.58Number of plants in 1947 6.9459 4.6538 19.5 1.03 0.306 3.42Number of R&D institutes in 1959 0.9054 0.3846 26.4 1.38 0.171 4.35Area in km2 0.8135 4.1237 -27.1 -1.65 0.102 0.25
Notes: For each variable, the table reports its mean for both the treated and control groups of thematched sample in question, along with the corresponding standardised bias (the treated-control dif-ference as a percentage of the combined standard deviation of the matched sample). In addition, foreach variable the table reports the t-statistic and the associated p-value about the difference in the twomeans, as well as the ratio between the variances of the treated and the variance of the control groupfor the variable in question. Both matched samples are obtained via 1:1 nearest neighbour matching onthe estimated propensity score (with replacement).
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Table E.3: Covariate Balance: Mahalanobis Matching, Science Cities with no “official” status intoday’s Russia
Matched sample: non-SME outcomesVariable Treated Control Std. bias t-stat. p-value V-ratioLatitude 55.698 54.649 24.2 1.43 0.155 0.87Longitude 50.548 50.836 -1.3 -0.07 0.941 1.06Average temperature in January (°C) -11.829 -11.875 1.1 0.06 0.949 0.74Average temperature in July (°C) 18.520 18.546 -1.3 -0.07 0.941 0.67Average altitude 0.1714 0.2507 -32.1 -1.96 0.052 0.23Minimum distance from coast 0.7227 0.7108 2.8 0.16 0.870 0.95Minimum distance from USSR border 0.6767 0.6786 -0.5 -0.03 0.977 0.87(Log) Population in 1959 3.0551 3.6003 -31.6 -1.80 0.075 2.26(Log) Number of universities in 1959 0.2067 0.1183 19.2 1.10 0.273 2.78(Log) Number of plants in 1947 1.2676 0.8206 37.6 2.18 0.031 1.52(Log) Number of R&D institutes in 1959 0.3228 0.1169 36.6 2.09 0.038 2.38(Log) Area in km2 -1.3477 -0.2071 -63.0 -3.68 0.000 0.98
Matched sample: SME outcomesVariable Treated Control Std. bias T-stat. p-value V-ratioLatitude 55.703 55.828 -3.4 -0.17 0.862 1.26Longitude 50.879 46.070 24.2 1.22 0.227 1.73Average temperature in January (°C) -11.888 -11.543 -8.4 -0.44 0.659 0.86Average temperature in July (°C) 18.516 18.410 5.8 0.31 0.761 1.00Average altitude 0.1725 0.1912 -10.6 -0.61 0.541 0.22Minimum distance from coast 0.7237 0.7466 -5.7 -0.30 0.768 1.25Minimum distance from USSR border 0.6765 0.7109 -9.7 -0.50 0.615 1.11(Log) Population in 1959 3.0083 3.6190 -35.0 -1.73 0.086 2.79(Log) Number of universities in 1959 0.2029 0.0996 23.0 1.13 0.260 3.25(Log) Number of plants in 1947 1.2224 0.8806 29.6 1.52 0.131 1.41(Log) Number of R&D institutes in 1959 0.3073 0.0630 48.0 2.33 0.022 6.00(Log) Area in km2 -1.3528 -0.42227 -50.2 -2.64 0.010 1.17
Notes: For each variable, the table reports its mean for both the treated and control groups of the matchedsample, along with the corresponding standardised bias (the treated-control difference as a percentage ofthe combined sample’s standard deviation). In addition, for each variable the table reports the t-statisticand the associated p-value about the difference in the two means, as well as the ratio between the vari-ances of the treated and the variance of the control group for the variable in question. Both matched sam-ples are obtained via 1:1 Mahalanobis matching (with replacement), forcing exact matching on the follow-ing dummy variables: presence of a major river, presence of a lake and presence of “historical” railroads ina municipality.
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