Introduction Background Data Methodology Results Conclusion

Are science cities fostering innovation?Evidence from Russia

Helena Schweiger1 Alexander Stepanov1 Paolo Zacchia2

1EBRD

2IMT Lucca

SITE Academic Conference: 25 years of transition, Stockholm,5 December 2016

Introduction Background Data Methodology Results Conclusion

Motivation

Innovation is a key driver of economic growth

Innovation tends to be spatially clustered (spillovers)

If innovation is an externality, what role for the government?

Either indirect (incentives) or direct (investment)

Both approaches can be place-based. A classical example isthe Silicon Valley, tracing roots in U.S. military investment

Their effect is difficult to evaluate

In Russia, a debate of particular relevance:

Innovation is essential to diversify the economy

Russia possesses excellent human capital resources as well as atradition of localized R&D policies: science cities

Assessing their effect is crucial to formulate growth policies

Introduction Background Data Methodology Results Conclusion

Research question

Question

Do innovation-focused place-based policies have an impact on localdevelopment? What is their effect on innovation and productivity,both at the municipal and firm level?

Contribution

1 First paper to evaluate the legacy of “innovation enclaves” inthe former Soviet Union on innovation in present-day Russia

2 We assess the impact on science cities both at the municipaland at the firm level, employing two unique datasets

3 Municipal level data: a combination of geographical, historicaland present characteristics of Russian municipalities

4 Firm-level data from BEEPS V: new and accurate measures ofproduct and process innovation

Introduction Background Data Methodology Results Conclusion

Preview of the results

Methodology

We employ a variety of matching methods to correct for:

the selection bias due to the government’s choice of location;

the self-selection of firms into science cities.

Main Results

1 Today’s science cities are responsible for larger patentproduction than otherwise similar “ordinary” municipalities

2 No evidence that, all else equal, firms in science cities aredifferent from firms in otherwise similar “ordinary”municipalities

Introduction Background Data Methodology Results Conclusion

Related literature

1 (Localised) knowledge spillovers:

Jaffe et al. (1993), Moretti (2004, 2011), Bloom, Schankermanand Van Reenen (2013), Lychagin et al. (forthcoming)

2 Evaluation of place-based policies:

Short-run: Neumark and Kolko (2010), Ham et al. (2011),Albouy (2012), Busso et al. (2013), Wang (2013)Long-run: Kline and Moretti (2014), Ivanov (2016)

3 Knowledge-focused place-based policies:

Felsenstein (1994), Westhead (1997), Siegel et al. (2003),Yang et al. (2009), Falck et al. (2010)

4 Military and R&D:

Moretti, Steinwander and Van Reenen (2015)

Introduction Background Data Methodology Results Conclusion

Innovation system in the Soviet Union

Best resources were allocated to sectors considered vital fornational security, military (2/3 of R&D spending)

Model: special-regime enclaves aimed at fostering innovation

Main research areas, in order of relevance:

Aviation, rocket and space scienceNuclear physicsElectronics, mechanicsChemistry and chemical physicsBiology and biochemistry

Civilian applications of technological advances were limited(example: lag in personal computer development in late years)

Introduction Background Data Methodology Results Conclusion

Innovation system in the Soviet Union (cont.)

1 Early 1930s: Experimental Design Bureaus (sharashki), partof the Soviet Gulag labour camp system

2 From mid-1930s: Science cities – localities with highconcentration of R&D facilities

About 2/3 established in existing cities/settlements, the restbuilt from scratchThose whose main objective was to develop nuclear weapons,missile technology, aircraft and electronics, were closed,represented only on classified maps, to maintain security andprivacyAcademic towns, to foster development of Siberia

Introduction Background Data Methodology Results Conclusion

Location of science cities

Note: BEEPS regions refer to regions covered in BEEPS V Russia.

Introduction Background Data Methodology Results Conclusion

Innovation system in Soviet Union/Russia

Soviet Union

R&D spending as % of GDP considerable, remarkable successeswere achieved but: no competition, lack of quality resources in thecivil economy, and bias towards large-scale projects, non-existentsmall enterprise sector

Russia

Public funding dried up with the collapse of the Soviet Union.Today: R&D spending much lower, almost 3/4 of R&D conductedby public organisations, funded mainly from the federal budget

State programs designed to support economic development andmodernization via technological innovation and commercialization ofinnovation since the early 1990s.

Science cities, innovation technology centres, technology parks,incubators, Special Economic Zones, Skolkovo

Introduction Background Data Methodology Results Conclusion

Three unique, interconnected datasets

1 Database with information on science cities, based onAguirrechu (2009) and publicly available information

2 Firm-level data at the plant level from the BEEPS V survey,including the novel innovation module.

37 Russian regions, 4220 face-to-face interviews conductedbetween August 2011 and October 2012Additional information allows more accurate measurement ofproduct and process innovation

3 Municipal-level data of Soviet/Russian municipal units (raionlevel), containing geographical, climate, population,education, innovation and economy-related data, both historicand present-day

Introduction Background Data Methodology Results Conclusion

Municipal-level database

1 Geographical data: Location of coastline, lakes and rivers,railroads in 1943, average monthly temperatures 1960-1990,area of the municipality, GPS coordinates of the centre ofmunicipality and various distances calculated in QGIS

2 Data on factories, research and design establishments of theSoviet defence industry from Dexter and Rodionov (2016)

3 Population data from the first post-World War II censusconducted in January 1959

4 Number of higher education institutions in 1959 from De Witt(1961) and number of R&D institutes in 1959 from variousopen sources (primarily Wikipedia)

5 Population with higher education or PhD/doctoral degreesfrom 2010 Russian census

6 Patent data from WIPO/EPO/USPTO geolocated patents,2006-2015

7 Nighttime lights data from NOAA, 1992-1994 and 2009-2011

Introduction Background Data Methodology Results Conclusion

Methodology: Summary

Science cities and non-science cities are different in theirobservable characteristics

However: selection criteria of the Soviet government werearguably not very much influenced by market forces or internalpolitical considerations

Less potential for bias(es) caused by unobservable factors

To account for differences in the observables, we employmatching techniques:

1 For the municipal analysis, we use stratification and nearestneighbour propensity score matching

2 For the firm-level analysis, we argue that coarsened exactmatching (CEM) is preferable due to two-way selection

Introduction Background Data Methodology Results Conclusion

Matching municipalities

Ideal quasi-experiment for the municipal analysis: compareplaces that were chosen to become science cities (Dc = 1)against those that were marginally discarded (Dc = 0)

In this case: ATT = long-run effect

We match municipalities on a mix of historical and geographicvariables using two alternative methods of propensity scorematching:

Stratification matchingNearest neighbour matching

Introduction Background Data Methodology Results Conclusion

Matching municipalities (cont.)

Set of observable characteristics Xc for matching:

Geographical/climate characteristics: average temperature inJanuary and July; altitude; longitude, latitude; access to anddistance from lake, river and coast; distance from border; areaof the municipalityHistoric characteristics: population in 1959; access to anddistance from railroad in 1943; number of higher educationinstitutions in 1959; number of scientific R&D institutions in1947; number of defence plants (except R&D) in 1947

Main outcomes of interest Yc :

Total and fractional patentsNighttime lights, 2009-2011Total population, population with higher education andpopulation with PhD and doctoral degrees from the 2010Russian Census

Introduction Background Data Methodology Results Conclusion

Matching firms in municipalities

Firm-level analysis: a two-way (dual) selection problem

Firm location choice is endogenous. Preference for sciencecity location Dic changes with firm characteristics Zic

But firms also choose over other municipal characteristics Xc ,which co-vary with Dic

Thus: simply matching over Zic is biased. Group differencesmay be due to differences in Xc (e.g. population, education)rather than science city status Dic

Further complications: a. Zic poorly predicts Dic , while b. Xc

identically predicts it for firms in the same city c

Common support issues, propensity score matching techniquesunderperform

Introduction Background Data Methodology Results Conclusion

Matching firms in municipalities (cont.)

We apply CEM over both Xc and Zic : similar firms in similarcities

Iacus, King and Porro (2009, 2011, 2015), King and Nielsen(2016) argue for CEM against propensity score matchingIn particular, propensity score matching weak when thepropensity score model has little power

Covariates employed for CEM:Firm-level characteristics Zic : Size, age, foreign and/or statecontrolled, market scope (local, national, international), % ofuniversity educated employees, manufacturing/retail dummyMunicipal-level characteristics Xic : Population in 2010,Postgraduate population in 2010, Fractional patentsWe force exact matching within administrative divisions

Outcomes Wic : 1. innovation and 2. productivity measures1 BEEPS innovation module: product and/or process innovation

dummies, R&D dummy, innovation-dependent share of sales.2 Solow residual / TFP, raw labor productivity

Introduction Background Data Methodology Results Conclusion

Matching municipalities: Common support(pre-matching)

Introduction Background Data Methodology Results Conclusion

Matching municipalities: Covariates balance

Introduction Background Data Methodology Results Conclusion

Municipal-level results: Stratification matching

(1) (2) (3) (4) (5) (6)All Frac. Night Population Graduates PhD

patents patents lights 2010 2010 2010ATT 32.77*** 11.82*** 6.141*** 30589.2 8212.4 186.6

(10.02) (4.471) (2.110) (31801.1) (8824.0) (234.3)Anal. S.E. [9.87] [4.36] [2.13] [30957.72] [8188.28] [230.41]Raw Diff. 37.11 13.57 23.71 79894.37 22509.07 596.18N. Treated 70 70 70 70 70 70N. Controls 1399 1399 1399 1399 1399 1399

Notes: Bootstrapped standard errors in parentheses. * = significant at the 10% level, ** = significant at the 5%level, *** = significant at the 1% level.

Introduction Background Data Methodology Results Conclusion

Municipal-level results: Nearest neighbour matching

(1) (2) (3) (4) (5) (6)All Frac. Night Population Graduates PhD

patents patents lights 2010 2010 2010ATT 34.47*** 11.55** 7.193** -3087.8 -143.8 -36.21

(10.14) (4.513) (3.641) (43328.9) (11779.2) (331.1)Anal. S.E. [9.92] [4.40] [3.74] [46529.55] [12294.66] [349.96]Raw Diff. 37.11 13.57 23.71 79894.37 22509.07 596.18N. Treated 73 73 73 73 73 73N. Controls 60 60 60 60 60 60

Notes: Bootstrapped standard errors in parentheses. * = significant at the 10% level, ** = significant at the 5%level, *** = significant at the 1% level.

Introduction Background Data Methodology Results Conclusion

Firm-level results

SolowResidual

LaborProductiv.

R&DDummy

InnovationDummy

ProductInnovation

Dummy

ProcessInnovation

Dummy

InnovationShare of

Sales

(1)ATT -0.019 0.068 -0.014 -0.020 -0.021 -0.001 0.280

(0.025) (0.096) (0.024) (0.033) (0.028) (0.027) (1.008)M 1494 1517 2154 2154 2154 2154 2113

(2)ATT -0.014 0.035 -0.007 -0.011 -0.021 0.010 0.477

(0.030) (0.115) (0.027) (0.037) (0.032) (0.030) (1.124)M 1205 1222 1711 1711 1711 1711 1676

(3)ATT 0.031 0.141 -0.061 -0.039 -0.007 -0.001 -0.854

(0.038) (0.168) (0.049) (0.040) (0.033) (0.039) (1.685)M 401 404 543 543 543 543 534

(4)ATT 0.041 -0.282 0.036 0.009 0.027 -0.009 -2.014

(0.051) (0.332) (0.060) (0.049) (0.037) (0.032) (2.278)M 74 76 99 99 99 99 98

Notes: * = significant at the 10% level, ** = significant at the 5% level, *** = significant at the 1% level. ATTeffects are calculated via weighted OLS, with weights associated to CEM-imputed strata, and by restricted sampleto observations belonging to matched stata following Iacus, King and Porro (2009, 2011). The size of the restrictedsample is denoted as M. Standard errors are clustered by municipality. Each set of results (1)-(4) is obtained byapplying a different CEM algorithm:

(1) CEM on all firm-level characteristics Zic (size, age, foreign and/or state controlled, market scope – local,national or international, manufacturing/retail dummy, % of university educated employees);

(2) CEM on all firm-level characteristics Zic , on modern municipal characteristics in Xc (fractional patents,population, PhD population) and on the number of R&D institutions from Soviet times;

(3) CEM as above in (2), forcing exact matching on the larger Russian economic regions;

(4) CEM as above in (2), forcing exact matching on Russian regions (e.g. oblast, krai).

Coarsening measures for the continuous variables in the dataset are available upon request.

Introduction Background Data Methodology Results Conclusion

Conclusion: Summary

Science cities are more innovative: they produce more patents,ceteris paribus

They still present a stronger concentration of R&D activitiesdespite the withdrawal of much of governmental support

Firms in science cities, however, no more likely to innovateand do not seem to be more productive or profitable ⇒understanding why is crucial for policy

Introduction Background Data Methodology Results Conclusion

Conclusion: Mechanisms

Mechanism: it is difficult to distinguish between: a. directgovernment intervention, b. persistence of human capital, c.knowledge spillovers

Ideas to motivate a story about a combination of b. and c.:

Low interregional mobility, significant part of workercompensation paid in kindSubstantial brain drain of scientists, researchers and engineersafter the collapse of the Soviet UnionHowever, many stayed in Russia, but left science and pursuedother career options, including opening a business

⇒ Knowledge spillovers upon transition to a market economy

Any explanation should address why innovation advantages donot transfer into productivity or broader economic advantages(institutional problems: the usual culprit)

Introduction Background Data Methodology Results Conclusion

Thank you for your attention!

schweigh@ebrd.com