PLENARY SESSION II The Role of Government in Building Absorptive Capacity.

PLENARY SESSION IIThe Role of Government in Building Absorptive Capacity

KEF VI: Technology Acquisition and Knowledge Networks

The Role of Government: Building Absorptive Capacity in Europe and Central Asia

April 17, 2007Mr. Itzhak GoldbergLead SpecialistEurope and Central Asia Region World Bank

The Importance of Innovation and Absorption for Growth

Definition: Absorptive CapacityFrom Absorption to Innovation: New to the Firm versus New to the WorldAbsorptive capacity: Firms capacity to assess the value of external knowledge and technology and make necessary investments and organizational changes to absorb and apply this in its productive activitiesExamples of absorption: Adopt new product, process, Upgrade old product, process; Quality certification, Technology license

Product v. Process Innovation Product Innovation - development of new products representing discrete improvements over existing ones.Process Innovation - redesign of products, services; reorganization of layouts, transport modes, management, HRDual economies: Indias and Israels islands of ICT or hi-tech in laggard conventional industry

Main Channels of AbsorptionTrade & FDIR&DLearning &Brain CirculationIPR &Knowledge BrokersAbsorption

Imports of machinery & equipment

Knowledge spillover from exporting to R&D-rich firms/countries

FDI vertical spillovers for suppliers (e.g. Lithuania), export diversificationChannel I: Trade and Supply Networks and Foreign Direct Investment (FDI)

Tertiary education and training by firms specialized skills to enable innovation

Instead of brain-drain, return migration brain circulation skills & entrepreneurship

India ICT example: Graduates of Public Technology Institutes (IITs) emigrated before 1990; Liberalization return expats - key to Brain Circulation and ICT miracleChannel II: Learning and Brain Circulation

Licensing technology reduce absorption cost compared to imitating around IPR

Clearer IPR reduce transaction costs for public-private and private-private technology transfers

Knowledge brokers complementary transfer of tacit knowledge Eg: Israeli incubators help Russian scientist-immigrants to commercialize new ideasChannel III: Intellectual Property (IP) and Knowledge Brokers

R&D for absorption, not JUST for innovation -- the second face of R&D la Cohen and Liebenthal (1990)

R&D output does not flow automatically or costlessly from developed countries to developing countries. China invests massively in R&D

Channel IV: Research and Development

Micro Firm-level Incentives: FDI Case studySlovenian Aluminum firm Impol bought Serbian Seval in 2002Post privatization: production increased by 3.5 times, productivity by 7.2 times and wages increased by 20%Why? Brownfield FDI changes micro firm-level incentives. More tomorrow at 2 PM from my colleague Dr. Goddard.

Measuring Absorption in ECAOverview of WB econometric study of absorption using BEEPS surveys of enterprises in ECA Absorption--adopting new and improved products and manufacturing technologies-- is more likely for firms that:Transition to export status increasing their absorption by about 33%, Form JV with a multinational increasing their absorption by 41%

Market failures may justify government intervention to stimulate absorptive capacity in private sectorBUTPolicy design needs to account for government failures: capture, corruption, misaligned incentives

ANDRole of Government the Why?

Skills and Human Capital India: Early publicly financed education - critical importanceInvestment Climate and GovernanceRussia: Poor investment climate (weak competition, red tape); governance: corruption, regional government capture by business

Pre-requisites for Intervention

The How of Government Intervention? Governments can support absorption via:Role I: Regulatory interventions to improve the policy environment for absorption

Entry, Financial Discipline, Exit, Mobility , Barriers to FDI, Trade: Non-Tariff-Barriers, Customs, Standards, Quality Certification and IPR Enforcement

Role II: Commitment of public funds financial and institutional instruments - next.

Which financial instruments?Matching grants: preserve private risk, no crowding out

Soft loans: maturity deters risk taking

Tax holidays: no profit to offset in SMEs

Guarantees: takes away risk

(Source: WB ECA Study Public Financial Support for Commercial Innovation)

Which Institutional Instruments? Institutional instruments include: Incubators Technology parks Technology transfer offices

Pros and cons of different options:Privately owned/managed & subsidizedPublicly owned & managedPublicly owned & privately managed & subsidized

Transition 2nd Phase Policy implications

Summary and KEF VI AgendaInnovation v. absorption; Product v. ProcessThe 4 channels: FDI/trade, skills, IP, R&D From Micro to Macro: Firm-level IncentivesThe Why of InterventionPre-requisites for InterventionThe How: regulate and/or subsidize?Financial and/or institutional instruments

Russian industrial firms:Absorptive Capacity and State Innovation PolicyVI Knowledge Economy Forum: Technology Acquisition and Knowledge Networks

17-19 April 2007, Cambridge , UK Boris Kuznetsov, Yuri Simachev Higher School of Economics, Interdepartmental Analytical Center, Moscow

Main issues:Technology transfer and innovation policy in Russia: short historical backgroundRussia now: R&D, innovation, productivity: quiet crisis of manufacturing against the background of economic growthInnovation activity of Russian industrial firms: Why dont they innovate?Options for innovation policy: What can the State do?

Data sourcesSurvey of competitiveness of large&medium manufacturing industrial firms:1000 manufacturing firms, 49 regions, Higher School of Economics and World Bank 2005-2006; Survey of R&D organizations:180 R&D institutes, Moscow, Interdepartmental Analytical Center, 2006;Survey of innovation activity of large&medium industrial firms: 570 industrial firms, All-Russia representative sample, Interdepartmental Analytical Center, Institute for Economi of Transition 2005;

Specific features of Russian innovation systemSoviet time inheritanceLarge-scale R&D sphere, separated from industryApplied research and development concentrated in a few high-tech sectorsBoth industry and R&D sector isolated from the rest of the WorldIndustrial enterprises functioned in non-competitive environment and had no in-build innovation incentives1990-ies inheritancePrivatization by itself in the monopolized economy did not created incentives to innovate Changes in the States priorities led to deep crisis of applied research and of high-tech sectorsGrowth of the 2000-ies was based on better management of the existing resources (capacities, labor) and oil revenues not on innovations

Total factor productivity in manufacturing: Russia versus other countries (Russia=100)

2 (2)

1

1.994

0.99

1.323

3.353

1.405

4.221

2.181

0.98

: -

1 (2)

1999*20002002200320042005Total

RussiaMedian L150584988

# firms268135159562

BICS

BrazilMedian L4444

# firms1.6351.635

ChinaMedian L236145172

# firms9971.4762.473

IndiaMedian L481923

# firms8681.7762.644

South AfricaMedian L9595

# firms581581

Total BICMedian L48407960

# firms8682.7733.6927.333

Developed OECDMedian L301928

# firms6933181.011

CEBMedian L1505320362032

# firms4124241081601.2012.305

BUKMedian L150502545

# firms2552435961.094

* Median employment in 1999 is based on the midpoint of a categorical response on firm size.

1999*20002002200320042005Total

RussiaMedian L150584988

# firms268135159562

BICS

BrazilMedian L4444

# firms1.6351.635

ChinaMedian L236145172

# firms9971.4762.473

IndiaMedian L481923

# firms8681.7762.644

South AfricaMedian L9595

# firms581581

Total BICMedian L48407960

# firms8682.7733.6927.333

Developed OECDMedian L301928

# firms6933181.011

CEBMedian L1505320362032

# firms4124241081601.2012.305

BUKMedian L150502545

# firms2552435961.094

* Median employment in 1999 is based on the midpoint of a categorical response on firm size.

Russia0.0%

: -

Russia100%

Brazil199%

China99%

India132%

South Africa335%

BICS141%

OECD422%

Cenral Europe218%

BUK98%

TFP level relative to Russia (%)

Brazil99.4%

China-1.0%

India32.3%

South Africa235.3%

Total BICS40.5%

Developed OECD322.1%

CEB118.1%

BUK-2.0%

1

5.8

5

3.2

2.1

1.7

1.6

1.3

1.1

1.1

1.2

1.4

1.5

1.7

1.8

2

New fixed capital to existent stock ratio (capital renewal coeff.)

(%)

2

5.810.8

511.3

3.211.98

2.112.69

1.713.41

1.614.3

1.315.16

1.116.09

1.117.01

1.217.89

1.418.7

1.519.4

1.720.1

1.820.7

221.2

&A

Page &P


Average age of equipment in Industry

3

10.85.8

11.35

11.983.2

12.692.1

13.411.7

14.31.6

15.161.3

16.091.1

17.011.1

17.891.2

18.71.4

19.41.5

20.11.7

20.71.8

21.22

&A

Page &P

Average age of equipment in Industry


4

7.131.461.5

71.824.53.7

29.34921.7

28.825.845.4

78.719.51.8

6123.815.2

Equipment, machinery, vehicles

Other industrial goods

Raw materials (incl. agriculture)

5

40.736.722.6

54.927.417.7

27.52943.5

42.631.426

43.616.639.8

60.919.519.6

Equipment, machinery, vehicles

Other industrial goods

Raw materials (incl. agriculture)

6

8060

10095

7050

10070

6555

5040

5035

8060

10085

4020

3020

4020

4060

3050

1990

2005

(%)

1

199019911992199319941995199619971998199920002001200220032004

Average age of equipment in Industry10.811.311.9812.6913.4114.315.1616.0917.0117.8918.719.420.120.721.2

New fixed capital to existent stock ratio (capital renewal coeff.)5.85.03.22.11.71.61.31.11.11.21.41.51.71.82.0

Equipment, machinery, vehiclesOther industrial goodsRaw materials (incl. agriculture)

Russia7.131.461.5

China71.824.53.7

India29.34921.7

Brazil28.825.845.4

Japan78.719.51.8

USA6123.815.2

Equipment, machinery, vehiclesOther industrial goodsRaw materials (incl. agriculture)

Russia40.736.722.6

China54.927.417.7

India27.52943.5

Brazil42.631.426

Japan43.616.639.8

USA60.919.519.6

19902005

8060

.. 10095

7050

.. 10070

6555

5040

.. 5035

8060

- 10085

4020

3020

4020

4060

3050

7

25.83333

48.11111

59.61727

55.85096

67.0527

43.21154

77.4127

64.92

(%)

2

-> okonh_2 = 11

Variable Obs Mean Std. Dev. Min Max2674.16667

4851.88889

q12_1 12 74.16667 33.4279 10 1006040.38273

5644.14904

_______________________________________________________________________________ 6732.9473

-> okonh_2 = 12 4356.78846

7722.5873

Variable Obs Mean Std. Dev. Min Max6535.08

q12_1 27 51.88889 35.41222 0 100

_______________________________________________________________________________

-> okonh_2 = 13

Variable Obs Mean Std. Dev. Min Max

q12_1 35 46 40.38273 0 100

_______________________________________________________________________________

-> okonh_2 = 14


q12_1 208 44.14904 41.96838 0 100

_______________________________________________________________________________

-> okonh_2 = 15


q12_1 38 32.94737 39.29167 0 100

_______________________________________________________________________________

-> okonh_2 = 16


q12_1 52 56.78846 45.37225 0 100

_______________________________________________________________________________

-> okonh_2 = 17


q12_1 63 22.5873 39.06018 0 100

_______________________________________________________________________________

-> okonh_2 = 18


q12_1 50 35.08 39.20893 0 100

_______________________________________________________________________________

-> okonh_2 = 19


q12_1 10 75 39.01282 0 100

3

IndicatorCoef.Std. Err.zP>z

FIG (firm belong to FIG)0.1680.16710.315

Fact of export in 20040.9490.1934.910

Export more than 10% of sales0.7080.2213.210.001

Export/Sales ratio0.0380.013.850

Firm invested in fixed assets0.2890.1541.870.061

Introduced new product-0.0440.157-0.280.779

Introduced new technology0.2490.1691.480.139

Fact of R&D expenditures0.280.1641.710.087

R&D/Sales ratio-3.42.148-1.580.114

ISO (Introduced ISO)0.5980.173.520

Patents/Know-How0.0310.1740.180.859

Share of workers with higher educ.0.0020.0060.330.741

MBD001C944C.xls

Sheet: Sheet1

Sheet: Sheet2

Sheet: Sheet3

Sheet: Sheet4

Sheet: Sheet5

.2

.2

.2

Export: volume and structure

1999

(. ) ( ). . . - () . - . .. . . . . . . . * . (./.).% . .. % . %

[1]244924457511448847001304359875/130770001838.122057.2250,356167.3157.9111

22272273493693961129028773/6760002238.926866.813,466189.2154.6116.8

263345518155906122107439396/5030003947.647371.216,318215.8258.1111.9

17413563171464252102671379/8390001853.422240.810540155.2149.2124.1

444570440186894552257675888/47150001393.51672224254149.6139.9117.2

403224839166426415236798424//

. - 1149307025286766062063042/10570001396.316755.611085167.7183.9117.8

418252861584662925978657/86300082398762142156.0174.3112.3

310927529105186955205740574/14390001809.52171439308162.6192.2103.6

-876786735

* 2001 . 2003 -

15724578400.1453652189

0.6420174841

1

0.0818370278

0.1361762647

0.0624204795

0.1632431213

0.1453652189

0.0461772788

0.0138412445

0.091875588

2000

0.9000576328

(, ) ( ), , - , - () , - , ., , , , , , , . * , (./.),% , ,, % , %

[1]3806711646,0017857607902020950856,0011965366.1713,294,0002735.732828.4448,215131.6147.6111.9

364228302,00144637689219590613,00777033.25711,00025213025223,396129.2156.7115.7

412997373,00245558929167438444,00620443.92560,0006180.57416632,121108.7145.8115.8

259583453,0099144397160439056,00773342.93877,0002625.831509.618521131.3139.4113.1

689536525,00272709440416827085,004134196.684,745,0002105.225262.434309128.0136.5120

613400411,00236976619376423792,003552751.38/

, - 164318862,007193921192379651,00813179.431,102,0002004.824057.617833124.6138.2113.4

59319934,002188618437433750,00683582.08849,0001209.114509.22247122.3131.1120.9

429089674,00139093101289996573,001214122.781,484,0002392.728712.442161118.6125.9114.4

* 2001 , 2003 -

2001

0.9040876735

(, ) ( ), , - , - () , - , ., , , , , , , . * , (./.),% , ,, % , %

[1]4549244165,0020067403582542503807,0012008092.4813,282,000401648192581,755110.7119.4104.9

392814090,00130773473262040617,00783668.92727,0004827.857933.630,956103.5110.699.8

408915392,00226431149182484243,00637452.92582,0008090.59708645,68689.491.8104.9

298196898,00102590153195606745,00775653.5877,0003703.144437.226768110.5124.0106.5

913099762,00373304082539795680,004159064.754,685,0003152.937834.843589116.5120.2107.2

803621292,00321016498482604794,003556791.83/

, - 185633959,0075039631110594328,00794968.331,054,0002742.932914.816683107.7113.8102.6

70644181,002575804244886139,00669158.17814,0001756.621079.22734110.9116.5105

561065222,00185970984375094238,001230266.421,492,0003384.940618.848483115.0120.3108.4

* 2001 , 2003 -

2002

0.8974961912

(, ) ( ), , - , - () , - , ., , , , , , , . * , (./.),% , ,, % , %

[1]53617021452513227942284847420311565135.9212,886,0005128.661543.2655,262117.2111.4103.7

472973381172074815300898566737921695,0006055.372663.628,908123.4108.5103

442841244247937264194903980619004.33570,0009526.511431848,046127.3113.4106

322416062111179389211236673746154.67866,0004571.754860.427663108.5104.9101.6

10520423404352345176168078233992616.754,510,0004240.650887.254897108.7112.3102

9183775903706234895477541013403482.92/

, - 21486601686184696128681320742521.831,010,0003493.341919.624405109.3106.0102.4

780821502974893648333214627738.33765,0002279.827357.63215105.3106.996.6

6709241452320736234388505221226705.831,495,0004280.251362.466868106.0107.6106.5

* 2001 , 2003 -

2003

0.876664702

(, ) ( ), , - , - () , - , ., , , , , , , . * , (./.),% , ,, % , %

[1]66034446222971368946363207567610856615.6712,384,0006439.177269.2752,079112.9115.4107

682342091244312673438029418697124.58664,0007901.694819.234,083129.3137.8108.9

541608474291140433250468041596320.58553,00011578.1138937.252,251127.3121.7106.2

( - )372753285121371973251381312700763829,0005792.169505.231396114.0112.0104.4

( )12745557105100997547644559563742076.424,317,0005367.864413.659152111.2109.9109.4

10986139184273757316712381873169280.92/

, - 25333029392221092161109201687096.67968,0004321.951862.824270108.0109.7101.5

851659323015451055011422531941.75694,0002782.333387.62912114.9110.097.7

8199511432499846145699665291197264.421,488,0005253.963046.864111113.8108.6105.1

* 2001 , 2003 -

2004

233,025

0.8428240876

(, ) ( ), , - , - () , - , ., , , , , , , . * , (./.),% , ,, % , %

87772306034074371533470285907010281097.8312,198,3918060.896729.6873,382128.4124.2106.1

1128476012451051290677424722686404.83/9353.4112240.874,557168.0154.2105

689811112364015608325795504564707.17/13449.1161389.259,646113.8123.2103.6

( - )471895787165363486306532301659431.42/7224.686695.238343.6125.7119.1107.4

( )159304757858801877610050288023603610.17/6684.980218.863906115.0114.8111.7

13453205684761265528691940163017839.92/

, - 295242621107300645187941976629664.42/53996478829822.8111.2110.1103

916988113099167560707136450085.08/3362.140345.24260.4110.1114.092.5

10068409253000064097068345161140500.5/6574.278890.466036.2111.3115.5104

* 2001 , 2003 - 396.297321586714.1534757711.1794563142

52770128462482874441413945897310752243.51230.916872258.24703115180.687252596

0.6012161563166780754331362387479501131.51175.53778107856.26920646710.5224338723

11

8.2%11.1%-2.89%-35%

13.6%8.9%4.68%34%

( - )6.2%4.1%2.18%35%

( )16.3%14.4%1.89%12%

14.5%11.7%2.85%20%

, - 4.6%2.6%1.98%43%

1.4%0.8%0.62%45%

9.2%7.4%1.82%20%

1

1999199919991999199919991999

0.9897108771.00978991770.88944232510.91289685220.87239677350.88546545870.8477780639

0.79630350020.82859740320.81900658151.10046354730.80978768540.92735607911.0086810747

0.83663414240.72445015120.70870468321.01447314290.74262563680.85519355461.0050641243

1.00470732170.71952274310.6543871020.98944753280.6721177170.73319679310.9157070712

1.35274348950.65608175270.65020737230.80389858820.57031312580.54955261840.8014377078

Ferrous metals

Non-ferrous metals

Chemical

Machinery

Wood

Light

Food

2

0.9897108771.00978991770.88944232510.93549613680.87239677350.88546545870.8477780639

0.79630350020.82859740320.81900658151.13955860180.80978768540.92735607911.0086810747

0.83663414240.72445015120.70870468321.06085630220.74262563680.85519355461.0050641243

1.00470732170.71952274310.6543871021.0516316390.6721177170.73319679310.9157070712

1.35274348950.65608175270.65020737230.88408836410.57031312580.54955261840.8014377078

Ferrous metals

Non-ferrous metals

Chemical

Machinery& metal processing

Wood

Light

Food

3

0.9897108771.00978991770.88944232510.93549613680.87239677350.88546545870.8477780639

0.79630350020.82859740320.81900658151.13955860180.80978768540.92735607911.0086810747

0.83663414240.72445015120.70870468321.06085630220.74262563680.85519355461.0050641243

1.00470732170.71952274310.6543871021.0516316390.6721177170.73319679310.9157070712

1.35274348950.65608175270.65020737230.88408836410.57031312580.54955261840.8014377078

1

199920002001200220032004

Industry114488470017857607902006740358251322794229713689464074371533

Ferrous metals93693961144637689130773473172074815244312673451051290

Non-ferrous metals155906122245558929226431149247937264291140433364015608

Chemical7146425299144397102590153111179389121371973165363486

Machinery& metal processing186894552272709440373304082435234517510099754588018776

Machinery166426415236976619321016498370623489427375731476126552

Wood5286766071939211750396318618469692221092107300645

Light158466292188618425758042297489363015451030991675

Food105186955139093101185970984232073623249984614300006409

199920002001200220032004

11.55977347761.75278816982.19517995312.59534339663.5587614482

11.54372477651.39575135481.83656249732.60756051294.8140913799

11.57504353161.4523557261.5902984491.86740859992.3348384485

11.38732854851.43554504711.55573431321.6983592442.3139329297

11.45916206271.99740483612.32877048762.72934522993.1462595871

11.42391229781.92887948712.22695110632.56795612042.8608833039

11.36074134921.41938627511.63019691051.74437627842.0296083655

11.38112553781.62545876481.87730374711.90289745541.9557266722

11.3223417391.76800425492.20629661732.37657430052.8521256177

199920002001200220032004

11111

0.9897108770.79630350020.83663414241.00470732171.3527434895

1.00978991770.82859740320.72445015120.71952274310.6560817527

0.88944232510.81900658150.70870468320.6543871020.6502073723

0.93549613681.13955860181.06085630221.0516316390.8840883641

0.91289685221.10046354731.01447314290.98944753280.8038985882

0.87239677350.80978768540.74262563680.6721177170.5703131258

0.88546545870.92735607910.85519355460.73319679310.5495526184

0.84777806391.00868107471.00506412430.91570707120.8014377078

20002001200220032004

99%80%84%100%135%

101%83%72%72%66%

89%82%71%65%65%

94%114%106%105%88%

87%81%74%67%57%

89%93%86%73%55%

85%101%101%92%80%

2

199920002001200220032004

Industry/11,965,36612,008,09211,565,13610,856,61610,281,098-1,684,268-0.1407619555

Ferrous metals/777,033783,669737,921697,125686,405-90,628-0.1166339021

Non-ferrous metals/620,444637,453619,004596,321564,707-55,737-0.089833663

Chemical/773,343775,654746,155700,763659,431-113,912-0.1472975385

Machinery& metal processing/4,134,1974,159,0653,992,6173,742,0763,603,610-530,587-0.1283408969

Machinery/3,552,7513,556,7923,403,4833,169,2813,017,840-534,911-0.150562593

Wood/813,179794,968742,522687,097629,664-183,515-0.2256759126

Light/683,582669,158627,738531,942450,085-233,497-0.3415785856

Food/1,214,1231,230,2661,226,7061,197,2641,140,501-73,622-0.0606382495

9,015,9019,050,2338,692,6638,152,5877,734,404-1,281,497-0.1421374824

6,263,6366,251,1856,000,4495,585,5845,238,090-1,025,546-0.1637301088

199920002001200220032004

Industry001.00357083180.96311182970.93873653930.94698920390.8592380445

Ferrous metals001.00853975040.9416234090.944714380.98462290630.8833660979

Non-ferrous metals001.02741424240.9710588980.96335445670.94698588130.910166337

Chemical001.0029877690.96196906220.93916587030.94101917480.8527024615

Machinery& metal processing001.00601521210.95997946410.93724909110.9629974820.8716591031

Machinery001.00113727350.95689685610.93118754950.95221597460.849437407

Wood000.97760506560.93402693160.92535551450.91641314460.7743240874

Light000.978899520.93810157020.84739408860.84611722990.6584214144

Food001.01329654650.99710583830.97599961680.95258865210.9393617505

199920002001200220032004

Industry01111

Ferrous metals01.00495123860.97768855081.00636796421.0397403711

Non-ferrous metals01.02375857271.00825144911.02622452240.9999964915

Chemical00.99941901180.99881346331.00045734980.9936957792

Machinery& metal processing01.00243568290.99674766160.99841547851.0169043935

Machinery00.99757510060.99354698650.99195835091.0055193562

Wood00.97412662330.96980112050.98574570790.9677123465

Light00.97541647190.97403182190.90269639360.8934813897

Food01.00969110941.03529601391.0396949261.0059128955

20002001200220032004

Ferrous metals01.00495123860.97768855081.00636796421.0397403711


Chemical00.99941901180.99881346331.00045734980.9936957792


Wood00.97412662330.96980112050.98574570790.9677123465

Light00.97541647190.97403182190.90269639360.8934813897

Food01.00969110941.03529601391.0396949261.0059128955

4

160.61.40.78.6

31.612.415.86.6

10.90.10.70.98

30.70.20.60.728.1

21.60.54.8212.5

30.90.41.31.725

37.30.30.61.534.4

17.20.20.30.613.3

33.30.30.80.628

&A

Page &P

(%)

5

4.70.61.40.78.6

5.812.415.86.6

1.20.10.70.98

1.10.20.60.728.1

1.80.54.8212.5

2.50.41.31.725

0.50.30.61.534.4

2.80.20.30.613.3

3.60.30.80.628

Other

Raw materials

Metals

Fuel

Manufacturing

3

BRICSPSKSPG

Population (millions)1841441,0801,2964638484383

GDP (US$ billions)604581.4691.21,931.70212.8242.3679.71,039.902,740.60

GDP per capita at PPP in 2004 (2000 $)7,5319,1012,8855,41910,28611,92418,84023,01926,013

GDP growth p.a. 2000-20042.26.16.29.43.22.94.63.10.7

Share of employment in agriculture (%)19.81056.744.110.318.48.85.72.5

-2002-2003-2000-2002-2003-2003-2003-2003-2003

Exports of goods & services as % of GDP183519.13426.63944.125.738

Imports of goods & services as % of GDP13.422.322.531.427.14139.729.333.1

Merchandise exports (% of GDP)1631.610.930.721.630.937.317.233.3

of which:

Agricultural raw materials0.610.10.20.50.40.30.20.3

Food4.50.41.11.11.92.60.42.51.4

Fuel0.715.80.90.721.71.50.60.6

Ores and metals1.42.40.70.64.81.30.60.30.8

Manufactures8.66.6828.112.52534.413.328

Unclassifiednil5.30.10.1nilnilnil0.32.3

(%)1631.610.930.721.630.937.317.233.3

0.610.10.20.50.40.30.20.3

1.42.40.70.64.81.30.60.30.8

0.715.80.90.721.71.50.60.6

8.66.6828.112.52534.413.328

BRICSPSKSpG

Other4.75.81.21.11.82.50.52.83.6

Raw materials0.610.10.20.50.40.30.20.3

Metals1.42.40.70.64.81.30.60.30.8

Fuel0.715.80.90.721.71.50.60.6

Manufacturing8.66.6828.112.52534.413.328

Share of manufacturing industries in total industrial VA

1999

(. ) ( ). . . - () . - . .. . . . . . . . * . (./.).% . .. % . %

[1]244924457511448847001304359875/130770001838.122057.2250,356167.3157.9111

22272273493693961129028773/6760002238.926866.813,466189.2154.6116.8

263345518155906122107439396/5030003947.647371.216,318215.8258.1111.9

17413563171464252102671379/8390001853.422240.810540155.2149.2124.1

444570440186894552257675888/47150001393.51672224254149.6139.9117.2

403224839166426415236798424//

. - 1149307025286766062063042/10570001396.316755.611085167.7183.9117.8

418252861584662925978657/86300082398762142156.0174.3112.3

310927529105186955205740574/14390001809.52171439308162.6192.2103.6

-876786735

* 2001 . 2003 -

15724578400.1453652189

0.6420174841

1

0.0818370278

0.1361762647

0.0624204795

0.1632431213

0.1453652189

0.0461772788

0.0138412445

0.091875588

2000

0.9000576328

(, ) ( ), , - , - () , - , ., , , , , , , . * , (./.),% , ,, % , %

[1]3806711646,0017857607902020950856,0011965366.1713,294,0002735.732828.4448,215131.6147.6111.9

364228302,00144637689219590613,00777033.25711,00025213025223,396129.2156.7115.7

412997373,00245558929167438444,00620443.92560,0006180.57416632,121108.7145.8115.8

259583453,0099144397160439056,00773342.93877,0002625.831509.618521131.3139.4113.1

689536525,00272709440416827085,004134196.684,745,0002105.225262.434309128.0136.5120

613400411,00236976619376423792,003552751.38/

, - 164318862,007193921192379651,00813179.431,102,0002004.824057.617833124.6138.2113.4

59319934,002188618437433750,00683582.08849,0001209.114509.22247122.3131.1120.9

429089674,00139093101289996573,001214122.781,484,0002392.728712.442161118.6125.9114.4

* 2001 , 2003 -

2001

0.9040876735

(, ) ( ), , - , - () , - , ., , , , , , , . * , (./.),% , ,, % , %

[1]4549244165,0020067403582542503807,0012008092.4813,282,000401648192581,755110.7119.4104.9

392814090,00130773473262040617,00783668.92727,0004827.857933.630,956103.5110.699.8

408915392,00226431149182484243,00637452.92582,0008090.59708645,68689.491.8104.9

298196898,00102590153195606745,00775653.5877,0003703.144437.226768110.5124.0106.5

913099762,00373304082539795680,004159064.754,685,0003152.937834.843589116.5120.2107.2

803621292,00321016498482604794,003556791.83/

, - 185633959,0075039631110594328,00794968.331,054,0002742.932914.816683107.7113.8102.6

70644181,002575804244886139,00669158.17814,0001756.621079.22734110.9116.5105

561065222,00185970984375094238,001230266.421,492,0003384.940618.848483115.0120.3108.4

* 2001 , 2003 -

2002

0.8974961912

(, ) ( ), , - , - () , - , ., , , , , , , . * , (./.),% , ,, % , %

[1]53617021452513227942284847420311565135.9212,886,0005128.661543.2655,262117.2111.4103.7

472973381172074815300898566737921695,0006055.372663.628,908123.4108.5103

442841244247937264194903980619004.33570,0009526.511431848,046127.3113.4106

322416062111179389211236673746154.67866,0004571.754860.427663108.5104.9101.6

10520423404352345176168078233992616.754,510,0004240.650887.254897108.7112.3102

9183775903706234895477541013403482.92/

, - 21486601686184696128681320742521.831,010,0003493.341919.624405109.3106.0102.4

780821502974893648333214627738.33765,0002279.827357.63215105.3106.996.6

6709241452320736234388505221226705.831,495,0004280.251362.466868106.0107.6106.5

* 2001 , 2003 -

2003

0.876664702

(, ) ( ), , - , - () , - , ., , , , , , , . * , (./.),% , ,, % , %

[1]66034446222971368946363207567610856615.6712,384,0006439.177269.2752,079112.9115.4107

682342091244312673438029418697124.58664,0007901.694819.234,083129.3137.8108.9

541608474291140433250468041596320.58553,00011578.1138937.252,251127.3121.7106.2

( - )372753285121371973251381312700763829,0005792.169505.231396114.0112.0104.4

( )12745557105100997547644559563742076.424,317,0005367.864413.659152111.2109.9109.4

10986139184273757316712381873169280.92/

, - 25333029392221092161109201687096.67968,0004321.951862.824270108.0109.7101.5

851659323015451055011422531941.75694,0002782.333387.62912114.9110.097.7

8199511432499846145699665291197264.421,488,0005253.963046.864111113.8108.6105.1

* 2001 , 2003 -

2004

233,025

0.8428240876

(, ) ( ), , - , - () , - , ., , , , , , , . * , (./.),% , ,, % , %

87772306034074371533470285907010281097.8312,198,3918060.896729.6873,382128.4124.2106.1

1128476012451051290677424722686404.83/9353.4112240.874,557168.0154.2105

689811112364015608325795504564707.17/13449.1161389.259,646113.8123.2103.6

( - )471895787165363486306532301659431.42/7224.686695.238343.6125.7119.1107.4

( )159304757858801877610050288023603610.17/6684.980218.863906115.0114.8111.7

13453205684761265528691940163017839.92/

, - 295242621107300645187941976629664.42/53996478829822.8111.2110.1103

916988113099167560707136450085.08/3362.140345.24260.4110.1114.092.5

10068409253000064097068345161140500.5/6574.278890.466036.2111.3115.5104

* 2001 , 2003 - 396.297321586714.1534757711.1794563142

52770128462482874441413945897310752243.51230.916872258.24703115180.687252596

0.6012161563166780754331362387479501131.51175.53778107856.26920646710.5224338723

11

8.2%11.1%-2.89%-35%

13.6%8.9%4.68%34%

( - )6.2%4.1%2.18%35%

( )16.3%14.4%1.89%12%

14.5%11.7%2.85%20%

, - 4.6%2.6%1.98%43%

1.4%0.8%0.62%45%

9.2%7.4%1.82%20%

1

1999199919991999199919991999

0.9897108771.00978991770.88944232510.91289685220.87239677350.88546545870.8477780639

0.79630350020.82859740320.81900658151.10046354730.80978768540.92735607911.0086810747

0.83663414240.72445015120.70870468321.01447314290.74262563680.85519355461.0050641243

1.00470732170.71952274310.6543871020.98944753280.6721177170.73319679310.9157070712

1.35274348950.65608175270.65020737230.80389858820.57031312580.54955261840.8014377078

2

0.9897108771.00978991770.88944232510.93549613680.87239677350.88546545870.8477780639

0.79630350020.82859740320.81900658151.13955860180.80978768540.92735607911.0086810747

0.83663414240.72445015120.70870468321.06085630220.74262563680.85519355461.0050641243

1.00470732170.71952274310.6543871021.0516316390.6721177170.73319679310.9157070712

1.35274348950.65608175270.65020737230.88408836410.57031312580.54955261840.8014377078

3

0.9897108771.00978991770.88944232510.93549613680.87239677350.88546545870.8477780639

0.79630350020.82859740320.81900658151.13955860180.80978768540.92735607911.0086810747

0.83663414240.72445015120.70870468321.06085630220.74262563680.85519355461.0050641243

1.00470732170.71952274310.6543871021.0516316390.6721177170.73319679310.9157070712

1.35274348950.65608175270.65020737230.88408836410.57031312580.54955261840.8014377078

Ferrous metals

Non-ferrous metals

Chemicals

Machinery

Wood&pulp

Light

Food

1

199920002001200220032004

Industry114488470017857607902006740358251322794229713689464074371533

Ferrous metals93693961144637689130773473172074815244312673451051290

Non-ferrous metals155906122245558929226431149247937264291140433364015608

Chemical7146425299144397102590153111179389121371973165363486

Machinery& metal processing186894552272709440373304082435234517510099754588018776

Machinery166426415236976619321016498370623489427375731476126552

Wood5286766071939211750396318618469692221092107300645

Light158466292188618425758042297489363015451030991675

Food105186955139093101185970984232073623249984614300006409

199920002001200220032004

11.55977347761.75278816982.19517995312.59534339663.5587614482

11.54372477651.39575135481.83656249732.60756051294.8140913799

11.57504353161.4523557261.5902984491.86740859992.3348384485

11.38732854851.43554504711.55573431321.6983592442.3139329297

11.45916206271.99740483612.32877048762.72934522993.1462595871

11.42391229781.92887948712.22695110632.56795612042.8608833039

11.36074134921.41938627511.63019691051.74437627842.0296083655

11.38112553781.62545876481.87730374711.90289745541.9557266722

11.3223417391.76800425492.20629661732.37657430052.8521256177

199920002001200220032004

11111

0.9897108770.79630350020.83663414241.00470732171.3527434895

1.00978991770.82859740320.72445015120.71952274310.6560817527

0.88944232510.81900658150.70870468320.6543871020.6502073723

0.93549613681.13955860181.06085630221.0516316390.8840883641

0.91289685221.10046354731.01447314290.98944753280.8038985882

0.87239677350.80978768540.74262563680.6721177170.5703131258

0.88546545870.92735607910.85519355460.73319679310.5495526184

0.84777806391.00868107471.00506412430.91570707120.8014377078

20002001200220032004

Ferrous metals99%80%84%100%135%

Non-ferrous metals101%83%72%72%66%

Chemicals89%82%71%65%65%

Machinery94%114%106%105%88%

Wood&pulp87%81%74%67%57%

Light89%93%86%73%55%

Food85%101%101%92%80%

2

199920002001200220032004

Industry/11,965,36612,008,09211,565,13610,856,61610,281,098-1,684,268-0.1407619555

Ferrous metals/777,033783,669737,921697,125686,405-90,628-0.1166339021

Non-ferrous metals/620,444637,453619,004596,321564,707-55,737-0.089833663

Chemical/773,343775,654746,155700,763659,431-113,912-0.1472975385

Machinery& metal processing/4,134,1974,159,0653,992,6173,742,0763,603,610-530,587-0.1283408969

Machinery/3,552,7513,556,7923,403,4833,169,2813,017,840-534,911-0.150562593

Wood/813,179794,968742,522687,097629,664-183,515-0.2256759126

Light/683,582669,158627,738531,942450,085-233,497-0.3415785856

Food/1,214,1231,230,2661,226,7061,197,2641,140,501-73,622-0.0606382495

9,015,9019,050,2338,692,6638,152,5877,734,404-1,281,497-0.1421374824

6,263,6366,251,1856,000,4495,585,5845,238,090-1,025,546-0.1637301088

199920002001200220032004

Industry001.00357083180.96311182970.93873653930.94698920390.8592380445

Ferrous metals001.00853975040.9416234090.944714380.98462290630.8833660979

Non-ferrous metals001.02741424240.9710588980.96335445670.94698588130.910166337

Chemical001.0029877690.96196906220.93916587030.94101917480.8527024615

Machinery& metal processing001.00601521210.95997946410.93724909110.9629974820.8716591031

Machinery001.00113727350.95689685610.93118754950.95221597460.849437407

Wood000.97760506560.93402693160.92535551450.91641314460.7743240874

Light000.978899520.93810157020.84739408860.84611722990.6584214144

Food001.01329654650.99710583830.97599961680.95258865210.9393617505

199920002001200220032004

Industry01111

Ferrous metals01.00495123860.97768855081.00636796421.0397403711


Chemical00.99941901180.99881346331.00045734980.9936957792


Machinery00.99757510060.99354698650.99195835091.0055193562

Wood00.97412662330.96980112050.98574570790.9677123465

Light00.97541647190.97403182190.90269639360.8934813897

Food01.00969110941.03529601391.0396949261.0059128955

20002001200220032004

Ferrous metals01.00495123860.97768855081.00636796421.0397403711


Chemical00.99941901180.99881346331.00045734980.9936957792


Wood00.97412662330.96980112050.98574570790.9677123465

Light00.97541647190.97403182190.90269639360.8934813897

Food01.00969110941.03529601391.0396949261.0059128955

Russian manufacturing industry is loosing competitive battle not only to other countries but to other sectors of Russian economy as well.Russian industry is very much segmented: along with highly efficient firms each industry includes a significant share of highly inefficient ones.The inefficient enterprises are being protected by high entry barriers:Large territory and isolated regional marketsA lot of specific niche marketsCheap energyCheap accumulated fixed capitalUnfavorable investment climateAll those factors are of transient nature and cannot provide for sustainable growth

Innovation activity: Russia vs. EU

1

11

27

37

39

39

40

45

46

47

49

49

52

53

54

55

59

66

75

1

Russia11

Greece27

Spain37

United Kingdom39

Norway39

Italy40

Portugal45

France46

Sweden47

Luxemburg49

Finland49

Denmark52

Austria53

Iceland54

Netherland55

Belgia59

Germany66

Ireland75

2

3

MBD0006FB96.xls

Innovation and investment activity of firms26% of firms had neither R&D nor investments

1

IndustryEnergyMetalChemicalMachineryWood&pulpStone&clayLightFood

2.3%5.5%7.1%40.5%8.1%11.3%13.1%12.2%

Size group1-200201-500501-1000>1000

17.80%26.50%21.60%34.10%

% of firms

Any technology innovations reported82%

Systematic innovations41%

Any R&D45%

R&D exp. >2% of sales21%


Any investments63%

Investments in equipment55%

1

0.023

0.055

0.071

0.405

0.081

0.113

0.131

0.122

2

0.178

0.265

0.216

0.341

3

Factors of innovation activity Competitive pressureGlobalization:Export Import of materials and detailsFDIStrategyFinancial situation

Regression result(industry controlled)

4

0.0740.1030.11

0.20.2060.24

0.2070.2240.253

0.1650.1870.266

0.1160.1870.143

0.1230.0930.117

0.2630.3360.37

0.3540.4770.39

No R&D

100% own R&D

Outsourcing R&D

5

7.911.3

2023.8

20.825.2

1725.8

13.114.6

1111.3

28.937.1

38.239

No R&D or no outsourcing

Outsourcing R&D

1


2.3%5.5%7.1%40.5%8.1%11.3%13.1%12.2%

Size group1-200201-500501-1000>1000

17.80%26.50%21.60%34.10%

% of firms



Any R&D45%



Any investments63%


Probability of firm to innovate systematicallyCoef.Std. Err.ZP>z[95% Conf.

Interval]

Global integration strategy0.374690.1404732.670.0080.650.099

New markets strategy0.366560.1251172.930.0030.1210.611

Strong competition with import0.3546020.138882.550.0110.0820.626

Good financial situation(dropped)

Satisfactory financial situation-0.650750.247956-2.620.009-1.136-0.164

Poor financial situation-1.297640.278322-4.660-1.843-0.752

Empl. < 200-0.447750.205388-2.180.029-0.85-0.045

Empl. 200-500-0.293540.1753-1.670.094-0.6370.05

Empl. 500-1000(dropped)

Empl. > 10000.2817970.1635731.720.085-0.03880.602

Fuel&Energy(dropped)

Metal-0.47120.451709-1.040.297-1.3560.414

Chemicals-0.116250.447078-0.260.795-0.99250.76

Machinery-0.426470.397735-1.070.284-1.2060.353

Wood&pulp-0.573790.443915-1.290.196-1.4430.296

Stone&clay-0.893210.437859-2.040.041-1.7514-0.035

Light-0.436180.419902-1.040.299-1.2590.386

Food-0.395030.426068-0.930.354-1.230.44

Other-1.117960.586036-1.910.056-2.2660.03

_cons0.8986290.475321.890.059-0.0321.83

N of observation-5170.1703

No R&D100% own R&DOutsourcing R&D

No obstacle7.4%10.3%11.0%

Low quality20.0%20.6%24.0%

High prices20.7%22.4%25.3%

No complex services16.5%18.7%26.6%

Not oriented on client11.6%18.7%14.3%

Import is better12.3%9.3%11.7%

Lack of state support26.3%33.6%37.0%

Lack of information35.4%47.7%39.0%

No R&D or no outsourcingOutsourcing R&D

No obstacle7.911.3

Low quality2023.8

High prices20.825.2

No complex services1725.8

Not oriented on client13.114.6

Import is better1111.3

Lack of state support28.937.1

Lack of information38.239

0

0

Innovation policy incentivesIn case of implementing following measures which changes do you expect in the next to years:

Sales growthNew products growthExport growthInvestment growthR&D growthReady techn. purchase growth

3 to 2 years the period of depreciation9.6140.77.9245.3

100% depreciation of unsuccessful R&D9.59.60.411.131.94.9

Profit tax-base decrease by 30-40% of incrememtal R&D21.921.91.121.626.37.4

Subsidies for contracts with R&D organization11.121.63.21722.59.1

Co-financing the large-scale projects22.322.84.927.914.410.7

Subsidies for patenting5.69.812.67.56.111.6

investment premium14.712.81.230.47.47.4

State loans/guarantees for importing new technologies17.519.87.219.65.828.6

Probability of firm to innovate systematicallyCoef.Std. Err.ZP>z

Global integration strategy0.374690.1404732.670.008

New markets strategy0.366560.1251172.930.003

Strong competition with import0.3546020.138882.550.011


Satisfactory financial situation-0.650750.247956-2.620.009

Poor financial situation-1.297640.278322-4.660

Empl. < 200-0.447750.205388-2.180.029

Empl. 200-500-0.293540.1753-1.670.094


Empl. > 10000.2817970.1635731.720.085

N of observation-5170.1703

1

0.023

0.055

0.071

0.405

0.081

0.113

0.131

0.122

2

0.178

0.265

0.216

0.341

3

Conclusions from empirical testingThe lack of competition has strong negative influence on innovation. Competition with other national producers has a positive impact only on some innovations mostly concerned with lowering costs of production. Strong competition with foreign producers significantly and positively influence innovations.International trade has a positive impact on innovation activity of a firm. Influence is strong for export and positive while weak for purchasing imported materials and details.FDI has no or insignificant impact on innovations in general and negative impact on R&D activity.Firms with strategy oriented on new markets and integration into global economy are more active in innovations.Size of a firm do matter: additional efforts needed for facilitating innovations at medium-sized and small firms.

Why Industry and Science do not see each other:Low demand for R&D services and results is very much due to the lack of innovation infrastructureBoth sides would like more stimulus from the state and more information about the available opportunities.Industrial firms with experience of outsourcing of R&D services are more pessimistic than those without the experience: 29% against 18% complain about low quality, 29% against 15% complain about the lack of complex servicesR&D institutions agree with lack of complex services but note also the problem of Intellectual Property Rights.1 No obstacles2- Imported technologies are better and cheaper3- R&D organizations are not oriented on clients needs4- R&D organizations do not provide all the necessary services5- Low quality of results6- High prices of R&D services7 No state policy facilitating the purchase of technologies8 Lack of information about R&D results and new technologies9 IPR belong to the state10 Negative and unjust picture of Russian R&D in the media

1

1020.9

926

-38.455.9

-31.184.7

-21.917.5

-21.114.7

-19.332.2

-13.515.8

-11.114.7

-8.84.5

Industrial firms

R&D org.

38,4

31,1

21,9

21,1

19,3

13,5

11,1

8,8

1

Industrial firmsR&D org.

1020.9

926.0

8-38.455.9

7-31.184.7

6-21.917.5

5-21.114.7

4-19.332.2

3-13.515.8

2-11.114.7

1-8.84.5

2

3

4

5

Obstacles for cooperation with R&D sphere

4

0.0740.1030.11

0.20.2060.24

0.2070.2240.253

0.1650.1870.266

0.1160.1870.143

0.1230.0930.117

0.2630.3360.37

0.3540.4770.39

No R&D

100% own R&D

Outsourcing R&D

5

7.911.3

2023.8

20.825.2

1725.8

13.114.6

1111.3

28.937.1

38.239


Outsourcing R&D

1


2.3%5.5%7.1%40.5%8.1%11.3%13.1%12.2%

Size group1-200201-500501-1000>1000

17.80%26.50%21.60%34.10%

% of firms



Any R&D45%



Any investments63%



Interval]







Empl. < 200-0.447750.205388-2.180.029-0.85-0.045

Empl. 200-500-0.293540.1753-1.670.094-0.6370.05


Empl. > 10000.2817970.1635731.720.085-0.03880.602


Metal-0.47120.451709-1.040.297-1.3560.414

Chemicals-0.116250.447078-0.260.795-0.99250.76

Machinery-0.426470.397735-1.070.284-1.2060.353

Wood&pulp-0.573790.443915-1.290.196-1.4430.296

Stone&clay-0.893210.437859-2.040.041-1.7514-0.035

Light-0.436180.419902-1.040.299-1.2590.386

Food-0.395030.426068-0.930.354-1.230.44

Other-1.117960.586036-1.910.056-2.2660.03

_cons0.8986290.475321.890.059-0.0321.83

N of observation-517Pseudo R2-0.1703



Low quality20.0%20.6%24.0%

High prices20.7%22.4%25.3%







No obstacle7.911.3

Low quality2023.8

High prices20.825.2






0

0

1

0.023

0.055

0.071

0.405

0.081

0.113

0.131

0.122

2

0.178

0.265

0.216

0.341

3

State dominance in R&D support lead to lower incentive to commercialize and restructure

1

87.8

70.27

63.33

46.67

Russian clients

Share of R&D organizations expecting private demand for R&D growth in 2007-2009 pending on state support

1

3 ( 2006-2009 .) :

80

8.826.25105

29.412533.3325

61.7668.7556.6770

003.333.33

12.229.7333.3350

87.870.2763.3346.67

3.712.504.76

18.5220.8336.3642.86

77.7866.6763.6452.38

80

52.962.546.765.0

.87.870.360.043.3

.74.154.263.647.6

Share of the state in R&D ornization's revenues

80

Russian clients87.870.2763.3346.67

Foreign clients77.7866.6763.6452.38

State61.7668.7556.6770

2

3

1

30.57

23.62

51.95

/

2

43.95

30.71

57.14

4

5032

6249

22.524.6

5433

17.424.6

33.330.8

3

5728

6449

2118

5930

1423

3030

Restructuring options

5

3457

6552

2330

4748

1824

5839

6

3428

6549

2318

4730

1823

5830

20-50

>80

7

27.04

23.9

20.75

28.3

1

2113.3813.38

6742.6856.05

6943.95100

97.097.09

7962.269.29

3930.71100

85.195.19

5837.6642.86

8857.14100

/

30.6

23.6

52.0

44.0

30.7

57.1

62%11517.9

73%29.71540.9

- 73%372550.4

- 48%15031.1

44%7.6017.4

97157

114157

116158

76158

69158

,

503253383157345728

624964584864655249

22.524.623232421233018

543355503359474830

, 17.424.614182423

33.330.830583930

80

Sales of ready technologies to industry57345728

Joined R&D projects with industrial firms64655249

Mergers with industrial firm21233018

Expanding own production59474830

Creating "daughter firms" for commecialization14182423

Joined projects with foreign partners30583930

state1Freq.PercentCum.

80%28.3100

2

3

What can the Government do?Current trends in policySpecial zones type of innovation policy: Creating high-tech production zones and technological parks Special state programs for high-tech industries (aircraft, shipbuilding, etc.)State programs for specific technologies (nanotechnology, biotechnology)Creating innovation institutes for commercialization of technology (State Venture company)Tax-related measures to facilitate R&D in industry

Testing efficiency of innovation policy measures 3 to 2 years period to depreciate R&D100% of unsuccessful R&D depreciationprofit tax-base down by 30-40% of surplus R&Dsubsidies for contracts between industrial firms and Russian R&D organization up to 30% of the contract;co-financing by the state (up to 50%) the large-scale R&D project on the principle of risk and profits sharing;subsidies of patenting and (keeping valid) patents abroad;investment premium up to 10% of investments into fixed assetsstate loans or state guarantees for importing new technologies from abroad

Response rate for different incentives

4

0.0740.1030.11

0.20.2060.24

0.2070.2240.253

0.1650.1870.266

0.1160.1870.143

0.1230.0930.117

0.2630.3360.37

0.3540.4770.39

No R&D

100% own R&D

Outsourcing R&D

5

7.911.3

2023.8

20.825.2

1725.8

13.114.6

1111.3

28.937.1

38.239


Outsourcing R&D

1


2.3%5.5%7.1%40.5%8.1%11.3%13.1%12.2%

Size group1-200201-500501-1000>1000

17.80%26.50%21.60%34.10%

% of firms



Any R&D45%



Any investments63%



Interval]







Empl. < 200-0.447750.205388-2.180.029-0.85-0.045

Empl. 200-500-0.293540.1753-1.670.094-0.6370.05


Empl. > 10000.2817970.1635731.720.085-0.03880.602


Metal-0.47120.451709-1.040.297-1.3560.414

Chemicals-0.116250.447078-0.260.795-0.99250.76

Machinery-0.426470.397735-1.070.284-1.2060.353

Wood&pulp-0.573790.443915-1.290.196-1.4430.296

Stone&clay-0.893210.437859-2.040.041-1.7514-0.035

Light-0.436180.419902-1.040.299-1.2590.386

Food-0.395030.426068-0.930.354-1.230.44

Other-1.117960.586036-1.910.056-2.2660.03

_cons0.8986290.475321.890.059-0.0321.83

N of observation-517Pseudo R2-0.1703



Low quality20.0%20.6%24.0%

High prices20.7%22.4%25.3%







No obstacle7.911.3

Low quality2023.8

High prices20.825.2






0

0

Innovation policy incentivesIn case of implementing following measures which changes do you expect in the next to years:

Sales growthNew products growthExport growthInvestment growthR&D growthReady techn. purchase growth

3 to 2 years the period of depreciation9.6140.77.9245.3

100% depreciation of unsuccessful R&D9.59.60.411.131.94.9

Profit tax-base decrease by 30-40% of incrememtal R&D21.921.91.121.626.37.4

Subsidies for contracts with R&D organization11.121.63.21722.59.1

Co-financing the large-scale projects (matching grants)22.322.84.927.914.410.7

Subsidies for patenting5.69.812.67.56.111.6

Investment premium14.712.81.230.47.47.4

State loans/guarantees for importing new technologies17.519.87.219.65.828.6

1

0

0

0

0

0

0

0

0

2

0

0

0

0

3

Conclusions (1)Firms work in competitive environment. BUT: Firms oriented on internal market and competing with compatriots do not differ much in their innovation strategies from monopolistic firms. Integration into the world economy and orientation on expansion to new markets corresponds with higher innovation activity. Any kind of active protectionist policy would only facilitate the conservation of the current situation.Financially bad-off firms are mostly passive both in investments and innovation. This group should be stimulated to go out of markets.

Conclusions (2)The major obstacles in relatively low co-operation between national R&D system and industrial firms concerns low level of information (i.e. R&D institution should be pushed to more active commercialization of their results) and no state policy to low risks of large innovation projects. Considering responses of different groups to incentives the State policy should be aimed at solving two tasks: to create more incentives for leaders to be more innovative; and to involve more firms from the group oriented exclusively on imitation strategy into active innovations of conducting R&D to create new products and new technologies.

Conclusions (3)Russian state policy towards innovations suffers not so much from the lack of resources but rather from the lack of institutions and instruments that would stimulate demand for innovations and R&D on the side of business. The most demanded changes in innovation policy are tax R&D-related measures: lowering tax base in case of increase of R&D expenditures and depreciation of R&D expenditures for unsuccessful R&D. Also, high demand is for lowing innovation risks through state co-financing of large-scale innovation projects. The last measure is important, in particular, for firms not currently active in their own R&D.

Thank you for attention

The Role of Government inBuilding Absorptive Capacity

Ken WarwickDTIKnowledge Economy Forum Cambridge, 17 April 2007

Ken WarwickKnowledge Economy Forum

OutlineDefining and measuring absorptive capacityThe role of GovernmentUK examples:Trade and FDIEnterprise and Innovation Role of international institutions


Absorptive capacityThe capacity to acquire, understand, develop and exchange knowledge from external sources (competitors, collaborators, customers, public research base)

Concept relevant to both individual businesses/organisations and to regions/nations

Can be measured at firm and regional/national level


Measures show considerable variation even within developed economiesResearchers as % of total population G8 and China


Signs of progress in developing UK capacity Relative changes in HEI knowledge transfer, 1996-2003


What is the role of Government?Analysis on drivers and impacts of globalisation - challenges and opportunities for all businesses Framework conditions for businesses to succeed (e.g. macro stability, consistent competition policies)Targeted interventions to address incentive, information, co-ordination failuresInvest in knowledge creation and innovation infrastructure (e.g. funding of education and research system, measurement system, IP agencies, standard setting)Key policy challenge to ensure government provides right kinds of support


Economic evidence trade & FDIUK evidence that entry into exporting gives firms a step productivity boost average 34%; through learning from overseas customers, exposure to new ideas, technologies, up-grading approachTrade and inward investment can give rise to beneficial knowledge spilloversImportance of networks and reputation effectsDifficulties in gaining access to key knowledge networks can lead to lower levels of investment in R&DYoung innovative and high-growth potential firms unable to fulfil potential without capabilities and access to networks needed for internationalisation


SME access to international marketsBarriers to trade exist and hinder SME exportingLegal & regulatoryContactsInformationFixed costs Language & culturalBias of doing business with firms in own country

Some evidence SME exporters experience problems accessing finance


SME access to international marketsMarket failures barriers to exportinginformation problemsmissing marketscoordination failures

Wider benefits e.g. reputation effects, knowledge spillovers- which private sector unaided would not achieve


UKTI role in providing supportStrengthening internationalisation capabilities of innovative and high growth SMEsAccess to information and advice that private sector would not or could not provideAccess to contacts overseas, strengthening social networks for trade, including support for group participation in overseas trade fairs and missionsFacilitate beneficial co-operation e.g. showcasing UK capabilities, building UK reputation overseas


InnovationEnterpriseFramework conditionsGeneral business conditionsEase of firm creationRegulation (including IP)Public receptiveness to technologyAvailability of financeIncentives for innovation/enterpriseKnowledge exchange & exploitationEase of co-operation/collaboration (B2B, business-science links)Transit of information flowsInnovation infrastructure (metrology,standards)Demand for innovationReturn on investment (potential & actual)Business attitude and capacityResponsiveness of public services

Framework conditionsGeneral business conditionsEase of start-up and exitBetter Regulation Availability of financeAccess to marketsCulture (risk/failure v opportunity)Knowledge exchange & exploitationPersonal networksWork experienceEducation (inc enterprise education)Role modelsMigrationDemand for enterpriseNeed for independence/controlBusiness ideasOpportunity entrepreneurshipNecessity entrepreneurshipPublic services social enterprisesInnovation/Enterprise Environment:


Policies to increase capacity of research base to engage with businessHigher Education Innovation Fund encourage knowledge transfer from Higher Education Institutes (HEIs)HEIs receive allocation according to formula providing they submit an institutional planFinancial support used to develop capacity (Technology Transfer personnel, staff training); support spin outs or industrial collaboration.


Policies to raise absorptive capacity in businessAddress market failure that leads to private under-investment in R&D (spillovers):Tax incentives present in many OECD economies Most economies supplement with targeted programmes providing support for individual projects (UK example: Collaborative Research and Development programme).Build business awareness of value of interaction with external knowledge sources (possible information asymmetries):Demonstrations of value that skilled researchers can add to a business (UK example: Knowledge Transfer Partnerships).Networks bringing together business, researchers and other actors (UK example: Knowledge Transfer Networks)


UK Technology Strategy Board (TSB)Objective - to promote and support research into, and development and exploitation of, science and technology for business benefit for economic growth and quality of lifeResponsible for delivering Collaborative R&D Programme and Knowledge Transfer NetworksIdentifies priority and emerging technologies and the activities required to ensure wealth generationWorks to 5-10 year time horizonReports annually to DTI Secretary of State


Collaborative Research & DevelopmentProgramme run by DTI, builds on earlier programmes in place for 10-15 yearsProvides part funding of R&D projects (with maximum subsidy rates set by European state Aids framework)Qualifying projects must be collaborations (business-led, most also involve universities and SMEs)Typical project lasts up to 3 yearsUsually two calls for proposals per year in selected technology themes


Knowledge Transfer NetworksKnowledge Transfer Networks (KTNs) part of technology programme strategyNetworks bringing together business, researchers and other actors (e.g. Government, regions)Activities are part funded by government, part by their participantsOver 20 networks, involving 13,000 peopleTwo newest networks announced in Budget 2007: digital communication and creative industries.


Role of international institutionsInternational institutions also play number of important rolesTraditional role helping countries cooperate to reach international agreementsHelping build support for good governance/policies in member countriesRole around building absorptive capacity


International institutions - building absorptive capacityCan take a number of forms:Undertaking global studies (economies of scale)Providing third party expertise e.g. technical assistance, country missions and providing expert opinion on a policy areaFacilitating exchange of best practice between countriesImportant challenges in this work


Further [email protected]://www.dti.gov.uk/http://www.dti.gov.uk/innovation/technologystrategy/tsb/index.htmlhttps://www.uktradeinvest.gov.uk/

PLENARY SESSION IIIResponsive Education Systems and Skills for the Knowledge Economy

EDUCATION FOR GROWTH: NECESSARY BUT NOT SUFFICIENT?KEN MAYHEWSKOPE

PRODUCTIVITY, GROWTH, EDUCATION AND SKILLSSkills are the simplest, best, most direct way to boost productivitySkills investment is the quickest way to maintain productivity. Skills investment is the only way to maintain productivity. Mark Fisher, chief executive of the Sector Skills Development Agency, 2006.

THE MACRO EVIDENCE ON EDUCATION AND GROWTHChevalier et al (2002):

Growth and the LEVEL of education and training

Growth and the RATE OF CHANGE of education and training

THE EXAMPLE OF HIGHER EDUCATION INew Zealand and Egypt

Scotland and Switzerland

England

THE EXAMPLE OF HIGHER EDUCATION IIPrivate rates of return

Social rates of return

Social externalities

Economic externalities (Krueger & Lindahl, 2001)

PRODUCTIVITYMeasurement

Resonance with private sector managers?

Long term versus short term: Buchanan for Australia; Lloyd for the UK

Productivity and competitiveness

IF, PRODUCTIVITY WHY SKILLS? ILeitch Review

OMahoney and de Boer: Germany and the UK one fifth of the gap France and the UK one eighth USA and the UK almost none

IF, PRODUCTIVITY WHY SKILLS? IIUK Productivity and Competitiveness IndicatorsThe Five Drivers: Skills Investment Enterprise Competition InnovationWhat is missing?

THE UTILISATION OF SKILLSWork organisation

Management of the employment relationship To move forward, the Government will have to lose its current fixation with boosting the supply of skills and integrate the promotion of productive people management within all public bodies offering organisations advice and support on learning and skills and business development, including management skills training with a major people management component. (CIPD, 2006: 29).

SKILLS AS SCAPEGOATThe old scapegoats; the new scapegoatsMassive increase in the supply of skillsWhy so little pay off in terms of productivity?If current supply of skill is in balance with demand, what contribution to productivity can further increases in the supply of skill make?

TWO PROPOSITIONSAttempts to boost skills have to be accompanied by other policy measures

They have to be the right sort of skills

How does current policy connect with these two propositions?

IF, SKILLS, WHAT SKILLS?Impact of product market strategy

PRODUCT SPECIFICATIONThe concept of product specification: - the number of characteristics - customisation - frequent changes of characteristics

Associated production processes

WHAT SKILLS?The futility of the stocktaking approach

Level

Quality

Breadth

CONCLUSIONSCompetence and Competition, 1988

Leitch Review of Skills, 2006

Policy churning

HOW CAN POLICY BE IMPROVED?A more subtle appreciation of product strategy, of employment relations and of linkages

Expertise of policy makers

Degree of centralisation and relationship with other actors

**

Responsive Education Systems and Skills for the Knowledge Economy Using education as a lever to compete by working smarter, rather than working harder or cheaper Organisation for Economic Cooperation and Development (OECD)

Knowledge economy forum Andreas SchleicherHead, Indicators and Analysis DivisionOECD Directorate for Education

**

The world is flat (Thomas Friedman) Key competencies for tomorrows world

**

Key competencies for tomorrows worldThe personal computer enabled millions of individuals to become authors of their own content in digital formThe spread of the Internet and the emergence of the World Wide Web enabled more people than ever to be connected and to share their knowledgeThe emergence of software standards meant that people were able to seamlessly work together and upload and globalise content

**

Economy-wide measures of routine and non-routine task input (Levy and Murnane, 2007)

Chart1

00000

4.252.8-0.11.5-2.2

9.85.6-40.2-5

13.58-7.7-2.7-6

Complex communication

Expert thinking

Routine cognitive

Routine manual

Non-routine manual

Sheet1

Complex communicationExpert thinkingRoutine cognitiveRoutine manualNon-routine manual

196900000

19794.252.8-0.11.5-2.2

19899.85.6-40.2-5

199913.58-7.7-2.7-6

To resize chart data range, drag lower right corner of range.

**

Delivering high level skills.Quantity - A world of change.

**

Baseline qualifications A world of change Approximated by the percentage of persons with ISCED 3 qualfication born in the period shown below (2004)2412311114A1.2a

**

Growth in university-level qualificationsApproximated by the percentage of persons with ISCED 5A/6 qualfication born in the period shown below (2004)1222320A1.3a+2.9+3.5+3.7

**

The returns on high level qualificationsPrivate internal rates of return (RoR) for an individual obtaining a university-level degree (ISCED 5/6) from an upper secondary and post-secondary non-tertiary level of education (ISCED 3/4), MALES

**

The returns on high level qualificationsPrivate internal rates of return (RoR) for an individual obtaining a university-level degree (ISCED 5/6) from an upper secondary and post-secondary non-tertiary level of education (ISCED 3/4), MALESRising tertiary level qualifications seem generally not to have led to an inflation of the labour-market value of qualifications. In all but three of the 20 countries with available data, the earnings benefit increased between 1997 and 2003, in Germany, Italy and Hungary by between 20% and 40% (UK 9%).Growing benefits in many of the countries with the steepest attainment growth

**

The earnings advantage of educationRelative earnings of 25-64-year-olds with income from employment (upper secondary education=100)A9.1a

**

The earnings advantage of educationRelative earnings of 25-64-year-olds with income from employment (upper secondary education=100)In the UK, females with a tertiary-Type A qualification earn, on average, twice as much as females who completed only upper secondary education (OECD average 161%).In the UK, males without upper secondary education earn 71% of those with it (OECD average 80%).Rising tertiary level qualifications seem generally not to have led to an inflation of the labour-market value of qualifications. In all but three of the 20 countries with available data, the earnings benefit increased between 1997 and 2004, in Germany, Italy and Hungary by between 20% and 40% (UK 5%).

A9.1a

**

Where do high skills pay?Distribution of 25-64-year-olds by level of earningsEUUnited States

**

The driving forces of GDP per capita growth Average annual percentage change (1990-2000)

**

The driving forces of GDP per capita growth Average annual percentage change (1990-2000)Ireland, Korea, Mexico and Turkey were the only countries where demography made a significant positive impact on GDP per capita growthWhile declines in employment rates reduced growth in others

**

The driving forces of GDP per capita growth Average annual percentage change (1990-2000)But in almost all countries, the biggest contribution came from increased labour productivityin others it is beginning to act as a slight drag on growth While declines in employment rates reduced growth in others

**

Enhancements in human capital contribute to labour productivity growthAverage annual percentage change (1990-2000)

**

Delivering high level skills.Quality Getting the fundamentals right

**

Who will be safe from outsourcing, digitalisation and automatisation?The great collaborators and orchestratorsThe more complex the globalised world becomes, the more individuals and companies need various forms of co-ordination and management The great synthesisersConventionally, our approach to problems was breaking them down into manageable bits and pieces, today we create value by synthesising disparate bits togetherThe great explainersThe more content we can search and access, the more important the filters and explainers become

**

Who will be safe from outsourcing, digitalisation and automatisation?The great versatilistsSpecialists generally have deep skills and narrow scope, giving them expertise that is recognised by peers but not valued outside their domainGeneralists have broad scope but shallow skillsVersatilists apply depth of skill to a progressively widening scope of situations and experiences, gaining new competencies, building relationships, and assuming new roles. They are capable not only of constantly adapting but also of constantly learning and growingThe great personalisersA revival of interpersonal skills, skills that have atrhophied to some degree because of the industrial age and the InternetThe great localisersLocalising the global

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Average performance of 15-year-olds in mathematicsHigh mathematics performanceLow mathematics performance

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Mathematical literacy in PISAThe real worldThe mathematical WorldA real situation A model of realityA mathematical modelMathematical resultsReal results

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Mathematical literacy in PISAThe real worldThe mathematical WorldUnderstanding, structuring and simplifying the situationMaking the problem amenable to mathematical treatmentInterpreting the mathematical resultsUsing relevant mathematical tools to solve the problemValidating the results

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Mathematical literacy in PISAThe real worldThe mathematical WorldThe educators challengeThe skills that are easiest to teach and test are also the skills that are easiest to digitise, automatise and offshoreTeaching and evaluating skills in a context of real-world complexityexpert thinking the ability to structure a problemcomplex communication the ability to convey a particular interpretation of information

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Average performance of 15-year-olds in mathematicsLow average performanceLarge socio-economic disparitiesHigh average performanceLarge socio-economic disparitiesLow average performanceHigh social equityHigh average performanceHigh social equityStrong socio-economic impact on student performanceSocially equitable distribution of learning opportunitiesHigh mathematics performanceLow mathematics performance

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Durchschnittliche Schlerleistungen im Bereich MathematikLow average performanceLarge socio-economic disparitiesHigh average performanceLarge socio-economic disparitiesLow average performanceHigh social equityHigh average performanceHigh social equityStrong socio-economic impact on student performanceSocially equitable distribution of learning opportunitiesHigh mathematics performanceLow mathematics performance

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OECD (2004), Learning for tomorrows world: First results from PISA 2003, Table 4.1a, p.383.20Consistency in quality standardsVariation in the performance of 15-year-olds in mathematics

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Variation of performance between schoolsVariation of performance within schoolsConsistency in quality standardsVariation in the performance of 15-year-olds in mathematicsOECD (2004), Learning for tomorrows world: First results from PISA 2003, Table 4.1a, p.383.11114125

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Using the potential.Equality in outcomes and equity in opportunities.

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School performance and schools socio-economic background Russian FederationFigure 4.13School proportional to sizeStudent performance and student SESStudent performance and student SES within schoolsSchool performance and school SESOECDOECDOECD

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Student performanceSchool performance and schools socio-economic background - FinlandAdvantagePISA Index of social backgroundDisadvantageFigure 4.13Student performance and student SESStudent performance and student SES within schoolsSchool performance and school SESSchool proportional to size

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Money matters but other things do tooMexicoGreecePortugalItalySpainGermanyAustriaIrelandUnited StatesNorwayKoreaCzech republicSlovak republicPolandHungaryFinlandNetherlandsCanadaSwitzerlandIcelandDenmarkFranceSwedenBelgiumAustraliaJapanR2 = 0.28Cumulative expenditure (US$)Performance in mathematics

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Money matters but other things do tooMexicoGreecePortugalItalySpainGermanyAustriaIrelandUnited StatesNorwayKoreaCzech republicSlovak republicPolandHungaryFinlandNetherlandsCanadaSwitzerlandIcelandDenmarkFranceSwedenBelgiumAustraliaJapanR2 = 0.28Cumulative expenditure (US$)Performance in mathematicsSpending per student is positively associated with average student performancebut not a guarantee for high outcomesAustralia, Belgium, Canada, the Czech Republic, Finland, Japan, Korea and the Netherlands do well in terms of value for moneywhile some of the big spenders perform below-average

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High ambitions and clear standardsAccess to best practice and quality professional development

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Sympathy doesnt raise standards aspiration does PISA suggests that students and schools perform better in a climate characterised by high expectations and the readiness to invest effort, the enjoyment of learning, a strong disciplinary climate, and good teacher-student relationsAmong these aspects, students perception of teacher-student relations and classroom disciplinary climate display the strongest relationships

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Challenge and supportWeak supportStrong supportLow challengeHigh challengeStrong performanceSystemic improvementPoor performanceImprovements idiosyncraticConflictDemoralisationPoor performanceStagnation

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High ambitionsAccess to best practice and quality professional developmentDiagnostic knowledge and intervention in inverse proportion to successDevolved responsibility, the school as the centre of action

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Durchschnittliche Schlerleistungen im Bereich MathematikLow average performanceLarge socio-economic disparitiesHigh average performanceLarge socio-economic disparitiesLow average performanceHigh social equityHigh average performanceHigh social equityStrong socio-economic impact on student performanceSocially equitable distribution of learning opportunitiesHigh mathematics performanceLow mathematics performance

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Durchschnittliche Schlerleistungen im Bereich MathematikStrong socio-economic impact on student performanceSocially equitable distribution of learning opportunitiesHigh mathematics performanceLow mathematics performanceSchool with responsibility for deciding which courses are offeredHigh degree of autonomyLow degree of autonomy

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Durchschnittliche Schlerleistungen im Bereich MathematikStrong socio-economic impact on student performanceSocially equitable distribution of learning opportunitiesHigh mathematics performanceLow mathematics performanceEarly selection and institutional differentiationHigh degree of stratificationLow degree of stratification

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Strong ambitionsAccess to best practice and quality professional developmentAccountabilityDevolved responsibility, the school as the centre of actionIntegrated educational opportunitiesIndividualised learningAccountability

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High ambitionsAccess to best practice and quality professional developmentDiagnostic knowledge and intervention in inverse proportion to successIndividualised learningDevolved responsibility, the school as the centre of actionIntegrated educational opportunities

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Paradigm shiftsPrescriptionInformed professionUniformityEmbracing diversityDemarcationCollaborationProvisionOutcomesBureaucratic look upDevolved look outwardsTalk equityDeliver equityHit & missUniversal high standardsReceived wisdomData and best practiceThe old bureaucratic education systemThe modern enabling education system

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Further informationwww.pisa.oecd.orgAll national and international publicationsThe complete micro-level databaseemail: [email protected]

[email protected]

and remember:Without data, you are just another person with an opinion

Hubert Ertl: Learning to work or working to learn?Hubert Ertl: Learning to work or working to learn?

Sixth annual Knowledge Economy Forum Technology Acquisition and Knowledge Networks Cambridge, 16-19 April 2007Learning to work or working to learn? Changing contexts of company-based trainingDr Hubert ErtlDepartment of EducationUniversity of Oxford


Outline

Socio-economic changesChanges in the organisation of workNew competences and company-based trainingOrganisation of trainingInstructional designs and learning processesIntegration of working and learning


Overarching changes: MegatrendsSocio-economic Change Processes


Overarching changes: MegatrendsPost-modernService SocietyGlobalisation, InternationalisationChange as the NormIntercultural Environments Demographic ChangeKnowledge and Information SocietyChanges in the Organisation of Work


Changes in the organisation of workCompanyOrganisationKnowledge-based practiceCommunicationModularised production systemsGoing-to-market timeSimultaneous re-engineeringIntegration of marketsComplex data systemsProcess competenceResponsive managementGroup-based work organisationDissemination of knowledgeE-commerceNetworks of stakeholdersDiversification


Wanted: New competencescommunicationself-organisation (individually and in groups)delegation of tasks, advicedealing with non-routine

competence to learn and act independentlychanging attitudes towards learning challenges for company-based training


Challenges for company-based trainingMacro LevelMicro LevelOrganisation of training: Planning of

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PLENARY SESSION II The Role of Government in Building Absorptive Capacity.

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