The long-run effects of R&D place-based policies: Evidence from Russian science cities

Helena Schweiger, Alexander Stepanov, Paolo Zacchia 26 August 2021

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The effectiveness of public support for science and research and development (R&D) is a long-standing issue in the economics of innovation. Some specific innovation policies, such as the creation of local R&D clusters, are place-based (Martin et al. 2008, Inoue et al. 2013, Zhang 2014). To evaluate the overall impact of such interventions, one must assess the spatial extent of agglomeration effects such as knowledge spillovers. A key question is whether place-based policies can generate persistent effects after their termination, possibly because of agglomeration forces at work. Absent any long-run effects, the net welfare effect of place-based policies is as likely to be negative as it is to be positive (Glaeser and Gottlieb 2008; Kline and Moretti 2014).

In our recent paper (Schweiger et al. forthcoming), we study the legacy of a unique innovation and R&D place-based policy, namely, Russian ‘science cities’. Prompted by the technological and military competition between geopolitical blocs during the Cold War, the Soviet government created or developed 95 middle-sized urban centres in the territory of modern Russia with the purpose of concentrating highly specialised, strategic R&D facilities (see Figure 1 for their locations). It relocated scientists, researchers, and other high-skilled workers from elsewhere in the Soviet Union to work in the newly created establishments. Most of these science cities were specialised in military-related fields, such as nuclear physics, aerospace, and chemistry. Russia maintains a comparative technological advantage in these sectors to this day. 

Figure 1 Location of Science Cities and regional population density

Source: Schweiger et al. (forthcoming).

The institutional context of Russian science cities provides a unique opportunity to study long-run consequences of an exogenous spatial reallocation of highly skilled workers for two reasons. First, concerns about selection of locations with a better potential for economic development – typical of studies about innovation clusters in other countries – are greatly diminished. The allocation of resources in the Soviet command economy was managed following suboptimal, often erratic rules of thumb (Gregory and Harrison 2005). This appears most evident in the choice of location for the science cities (Agirrechu, 2009), which was based on a trade-off between secrecy and usability. The Soviet leaders prioritised places that offered better secrecy and safety from foreign interference (in the form of R&D espionage), or that were otherwise easy to control by governmental agencies, by virtue of geographical proximity. Strictly economic considerations did not play a role. 

Second, the transition to a market economy that followed the dissolution of the USSR resulted in a large negative shock to Russian R&D, including abruptly suspended state support for science cities. The latter was partially resumed in the 2000s for 14 of the former towns, bearing the official name of Naukogrady (Russian for “science cities”). By analysing historical science cities separately from modern Naukogrady, we are able to evaluate the extent to which the modern characteristics of the former depend on the long-run effects due to the Soviet-era policy, rather than on current government support.

These distinctive institutional features motivated us to build a unique, rich dataset covering geographical, historical, and present-day characteristics of Russian municipalities. With it, we estimate the effect of the past establishment of a science city on present-day municipal-level human capital (measured as the share of the population with either a graduate or postgraduate qualification), innovation (evaluated in terms of patent applications), and various proxies of economic development. To give our estimates a causal interpretation, we match science cities to localities that were similar at the time of selection in terms of characteristics that could affect both their probability of being chosen and their future outcomes and on a similar population growth trend. Our main identifying assumption is that, conditional on our matching variables, the choice of a locality was determined at the margin by factors independent from future, post-transition outcomes.

We find that, at present, Soviet-era science cities still host a more educated population, are more economically developed, employ a larger number of workers in R&D and ICT-related jobs, and apply for more patents than the comparable localities at the time of the programme's inception (see Figure 2). Moreover, workers in former science cities receive substantially higher gross monthly salaries: roughly US$250 per month for high-skilled occupations, and $100-120 per month for low-skilled occupations. The results remain largely unchanged when modern Naukogrady are excluded from the analysis. However, the patent filings and the share of workers employed in the R&D or ICT sectors point estimates decrease substantially, suggesting that current government support is an important stimulant for employment in R&D and ICT sectors. Analysis of demographic outcomes and economic development (proxied by night lights) reveals little to no evidence of mean reversion.

Figure 2 Municipal-level matching estimates, all and historical science cities

Source: Schweiger et al. (forthcoming).
Note: This chart summarises the results from Tables 1 and 2 in Schweiger et al. (forthcoming). It shows the relative bias adjusted average treatment effect on the treated (ATT) on various outcomes for the sample of all and historic science cities, respectively, together with a ±1 standard error interval. Both ATT and standard error are divided by the standard deviation of the outcome in the sample of all and historic Science Cities, respectively (hence “relative”). The numbers represent the actual estimated bias adjusted ATT (not the relative ones). Standard errors are computed following Abadie and Imbens (2006). sEmpl. - employment; Frac. - fractional; Avg. - average; No. - number.

We interpret the findings through the lenses of a spatial equilibrium model, inspired by Glaeser and Gottlieb (2009), Moretti (2011), and Allen and Donaldson (2020), that incorporates both path-dependence and agglomeration forces. The estimation of the equilibrium equations using the sample of matched cities reveals large estimates of the parameters that embody path-dependence as well as robust agglomeration elasticities stemming from a higher concentration of high-skilled workers. Furthermore, estimates tentatively suggest that the government needs to provide a subsidy equal to about 150% of the local ex-ante wage to high-skilled workers to reproduce allocations of labour mimicking that of former science cities. The results from this semi-structural analysis contrast with the study by von Ehrlich and Seidel (2018) on the long-run effect of the subsidies that West German municipalities formerly bordering the Iron Curtain used to receive. Their empirical analysis, unlike ours, rules out agglomeration effects; they propose persistence in public goods investment as the explanation of their measured long-run effects. We assess this alternative hypothesis by analysing budget data of Russian municipalities. We observe no evidence of differences in the overall expenditure or patterns in the use of resources between science cities and their matched counterparts.

Our contribution extends previous findings about long-run effects of place-based policies to a unique historical program that focused on human capital and R&D. More generally, our results are also informative for science and innovation policy, both in the context of emerging economies such as Russia and in those of traditionally capitalist countries. We hope that these results will be invoked to motivate similar R&D policies but with a civil, instead of military, purpose.

References

Agirrechu, A A (2009), Russian science cities: History of formation and development (Naukogradi Rossiyi: Istoriya formirovaniya i razvitiya), Moscow University Press (in Russian).

Allen, T and D Donaldson (2020), “Persistence and Path Dependence in the Spatial Economy”, NBER Working Paper No. 28059.

Glaeser, E L and J D Gottlieb (2008), “The economics of place-making policies”, Brookings Papers on Economic Activity, Spring: 155-239.

Glaeser, E L and J D Gottlieb (2009), “The wealth of cities: Agglomeration economies and spatial equilibrium in the United States”, Journal of Economic Literature 74(4): 983-1028.

Gregory, P R and M Harrison (2005), “Allocation under Dictatorship: Research in Stalin’s Archives”, Journal of Economic Literature 43(3): 721-761.

Inoue, H, K Nakajima, and Y Saito (2013), “Distance frictions and border effects in knowledge creation: Evidence from Japanese patent data”, VoxEU.org, 25 October.

Kline, P M and E Moretti (2014), “People, places, and public policy: Some simple welfare economics of local economic development programs”, Annual Review of Economics 6: 629-662.

Martin, P, T Mayer, and F Mayneris (2008), “Natural clusters: Why policies promoting agglomeration are unnecessary”, VoxEU.org, 4 July.

Moretti, E (2011), “Local labor markets”, in Handbook of Labor Economics, Elsevier.

Schweiger, H, A Stepanov, and P Zacchia (forthcoming), “The Long-run Effects of R&D Place-based Policies: Evidence from Russian Science Cities”, American Economic Journal: Economic Policy.

von Ehrlich, M and T Seidel (2018), “The persistent effects of place-based policy: Evidence from the West-German Zonenrandgebiet”, American Economic Journal: Economic Policy 10(4): 344-374.

Zhang, Hongyong (2014), “Agglomeration and product innovation in China”, VoxEU.org, 21 July.

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Senior Economist, EBRD

Associate Economist, EBRD

Assistant Professor in Economics, CERGE-EI

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