1. The Effects of Industrial Systems on Technology Adoption Joung Yeo No Yonsei University

2. Industrial Systems in Technology Adoption The advantages of geographical agglomeration on knowledge spillovers and technology adoption The advantages of geographical agglomeration on knowledge spillovers and technology adoption Not only the size of cluster matters Not only the size of cluster matters Does the organization of economic activities within a cluster matter as well? Does the organization of economic activities within a cluster matter as well?

3. What is Industrial Systems? Relationship between the internal organization of firms and their connections to one another and to the social structures and institutions of their particular localities Relationship between the internal organization of firms and their connections to one another and to the social structures and institutions of their particular localities Industrial system has 3 dimensions: Local institutions and culture Local institutions and culture Industrial organization Industrial organization Corporate organization Corporate organization

4. Effect of Industrial Systems on Technology Adoption The innovativeness of a region depends not only on the size and density of cluster, but also on how the economic activities within a cluster are organized. Silicon Valley vs. Boston’s Route 128 (Saxenian, 1994) → Silicon Valley - open, flexible and entrepreneurial environment with many small-, medium-sized plants. - decentralized regional network-based system → Route 128 - Rigid and hierarchical with few dominant players. - independent firm-based system

5. The Objectives 1. How the regional industrial system affects technology adoption by plants 2. How plants respond differently to geographical agglomeration and regional industrial system depending on their internal resources and information networks

6. Related Literature 3. Work on the effects of industrial systems (Saxenian 1996) – Descriptive studies on Silicon Valley and Route 128 1. Work on the sources of agglomeration (Rosenthal and Strange 2001; Dumais, Ellison, and Glaeser 2002; Holmes 2002). 2.Work on other types of knowledge spillovers – Patent citations (Jaffe, Trajtenberg, and Henderson 1993). Three relevant strands:

7. Do industrial systems affect plants’ decisions to adopt technologies?

8. Hypotheses H 1a: Adoption of advanced manufacturing technologies is more likely with an increase in employment at small plants in the region. H 1b: Adoption of advanced manufacturing technologies is more likely with an increase in employment at plants that are single-plant firms in the region.

9. Hypotheses H 2a: The effect of regional industrial system is greater for small plants than for large plants. H 2b: The effects of regional industrial system is greater for single-plant firms than for plants that are part of multi-plant firm.

10. Hypotheses H 3a: The effect of knowledge spillovers from prior adopters is greater for plants with less internal resources. H 3b: The effect of regional agglomeration is greater for plants with less internal resources. H 3c: The effect of knowledge spillovers from prior adopters is greater for plants that are single-plant firms. H 3d: The effect of regional agglomeration is greater for plants that are single-plant firms.

11. Main Finding I. Technology adoption is facilitated by the industrial system that are characterized as follow: 1. 1.That are agglomerated with small plants 2. 2.That are agglomerated with single-unit plants 3. 3.That are agglomerated with plants that are similar II. II.Plants with the following characteristics are more likely to benefit from the regional agglomeration and knowledge spillovers: 1. 1.Plants that are small 2. 2.Plants that are single-unit

12. DATA 1993 Survey of Innovation and Advanced Technology Unique, confidential, proprietary data Adoption of 22 advanced manufacturing technologies at the plant level 1902 plants covering an entire manufacturing sector across Canada Panel nature: years of use for each technology (1984-1993) → Panel of 3 intervals: 1984-1986, 1987-1989, and 1990-1992.

13. DATA (Cont’d) → Sample size: 1,902 plants, 22 technologies, 3 time periods ⇒ 106,188 obs. Annual Survey of Manufactures - Collects information on the universe of manufacturing plants in Canada. Census of Population - Demographic information National Input-Output Table - Input supply and output demand relationships among industries

14. Estimating Equation The probability of technology adoption is a function of: 1. Plant characteristics 2. Local amenities, industry, technology and time fixed effects 3. Regional agglomeration effects 4. Technology spillovers 5. Industrial Systems Dependent variable:

15. Estimating Equation Pr(Adoption pτirt ) =f (IndustrySystem rt, KnoweldgeSpillover τirt. RegionalAgglomeration rit, PlantCharacteristics prit, controls)

16. Technological Dimension Design & Engineering Fabrication & Assembly Automated Material Handling Inspection & Communication Manufacturing Information System Integration & Control CAD/CAE CAD/CAM Digital rep. of CAD output used in procurement Flexible manuf. cell or system NC/CNC Materials working laser Pick & place robots Other robots Automated storage and retrieval system Automated guided vehicle system Automated equip. for inspection of in-process Automated equip. for inspection of final LAN for technical data LAN for factory use Intercompany computer networks Programmable controller Computer for factory floor Material requirement planning (MRP) Manufacturing resource planning (MRP II) Computer integrated manufacturing (CIM) Supervisory control & data acquisition Aritifial intelligence & expert systems 6 technology groups 22 technologies

17. Computer Numerically Controlled Machine

18. Automated Guided Vehicle System

19. Automated Storage and Retrieval System

20. Pick and Place Robot Pharmaceutical

21. Pick and Place Robot Cream cheese

23. Geographical Dimension Geographical Dimension Rest of Country Province Economic Region 10 provinces 68 Economic Regions 290 Census Divisions Census Division

24. Map of Canada

25. Functional and Industrial Dimension 1. Industrial Dimension based on industry classification based on industry classification 2-digit (22) and 3-digit (169) SIC 2-digit (22) and 3-digit (169) SIC 2. Functional Dimension based on similarities in input purchases based on similarities in input purchases

26. A measure of ‘related’ industries: I develop a measure of ‘related’ industries based on the similarity of input purchases across industries. ρij = correlation between industry i and industry j in terms of pattern of input purchases For each industry i, all other industries are classified into three groups: Similar industries : 0.5 ≤ ρij Moderately similar industries: 0.2 ≤ ρij < 0.5 Different industries: ρij < 0.2

27. Summary Statistics of Sizes of ‘Related Industries’ Avg. No. of SIC-3 Industries Standard Deviation Similar industries6.655.61 Moderately similar industries8.894.02 Different industries92.469.60 SIC-2 Industry4.952.61

28. Technology Adoption Knowledge Spillovers Organizational Characteristics Demand Conditions Related and Supporting Industries Factor Conditions Industrial Systems

29. Plant Characteristics is a vector of plant characteristics which includes {Size, No. of commodities, Diversity, Foreign ownership, Single- or Multi-plant firm status}

30. Fixed Effects Region Industry Technology Time

31. Agglomeration Effects Employment in region r at time t-1 Share of scientists & engineers in in the population in region r at time t-1 Value of output of industry i’s input suppliers in region r at time t-1. Value of output of industry i’s output demanders in region r at time t-1.

32. Technology Spillovers # of adopters of tech τ in Similar industries in region r at time t-1. # of adopters of tech τ in Moderately similar industries in region r at time t-1. # of adopters of tech τ in Different industries in region r at time t-1.

33. Empirical Results I. Effects of Industrial Systems on Technology Adoption 1. Based on Plant Size 2. Based on Plant Status II. Effects of Regional Agglomeration Conditional on Organizational Capabilities 1. Plant Size 2. Plant Status

34. 1. Main Results Variable NamesCoefficientStd. ErrorElasticity TECH_Similar τirt-1.0388*.0030.0012 TECH_Moderate τirt-1.0214*.0030.00065 TECH_Different τirt-1 -.0187*.0043-.00057 Regional Emp r, t-1.0656*(.0087).002 Input ir, t-1.0760*(.0079).014 Output ir, t-1 -.139*(.0088)-.013 Engineer r, t-1 4.39*(.9700)1.07 Observations16,188 Log likelihood68,172 Dependent variable: ADOPTION pτirt Notes: 1) * χ 2 statistically significant at p < 0.05 2) Also included are plant characteristics, agglomeration effects, and fixed effects.

35. Plant Characteristics Variable NamesCoefficientStd. ErrorElasticity Size.557*.0076.017 Age-.0752*.009-.0023 Segment (# of 4-sic).100*.0085.003 Commodity-.115*.0091-.0035 Small(=1)-.407*.020 Foreign(=1).086*.015 Single(=1)-.182*.016 Observations16,188 Log likelihood67,936 Dependent variable: ADOPTION pτirt

36. I. Effect of Industrial Structure on Technological Adoption Based on Plant Size Variable Name Main Regional Employment Related Industry Employment Emp_region irt-1.0656* (.0087).0015 Small_region irt-1.0616* (.0102).0019 Large_region irt-1.0055 (.0072).00017 Small_related irt-1.0426* (.0074).0013 Large_related irt-1.0114 (.0033).00035 Observation105,902105,902105,902 Log Likelihood 67,93767,94867,960

37. I. Effect of Industrial Structure on Technological Adoption Based on Plant Status Variable Name Main Regional Employment Related Industry Employment Emp_region irt-1.0656* (.0087).0015 Single_region irt-1.1167* (.0122).0035 Multi_region irt-1 -.0430* (.0112)-.0013 Single_related irt-1.0403* (.0067).0012 Multi_related irt-1 -.0241* (.0035)-.00073 Observation105,902105,902105,902 Log Likelihood 67,93767,99968,003

38. I. Effect of Industrial Structure on Technological Adoption Small vs. Large Variable Name Main Small Plants Only Large Plants Only Emp_region irt-1.0656* (.0087).08866* (.0138).0843* (.0146) Small_region irt-1.0616* (.0102).0169* (.0153) -.1125* (.0259) Large_region irt-1.0055 (.0072) -.0024 (.0081).1978* (.0300) Observation105,902105,90277,15377,15328,74928,749 Log Likelihood 67,93767,94840,31839,83221,42021,323

39. I. Effect of Industrial Structure on Technological Adoption Single vs. Multi Variable Name Main Single Plants Only Multi Plants Only Emp_region irt-1.0656* (.0087).0741* (.0125).0491* (.0149) Small_region irt-1.1167* (.0122).0168* (.0178).0092 (.0231) Large_region irt-1 -.0430* (.0112) -.597* (.141).0457 (.0261) Observation105,902105,90271,527 34,37534,375 Log Likelihood 67,93767,99947,98148,04120,90020,902

40. II. Effects of Regional Agglomeration Conditional on Plant Size Variable Name(1)(2)(3)(4)(5) Tech Users in Related Ind.0355* (.0030).0280* (.0032).0336* (.0030).0360* (.0030).0297* (.0032) Regional Employment.0656* (.0087).0670* (.0088).0661* (.0088).0041* (.0088).0980* (.0156) Interaction Terms SMALL* Tech users_rel.0177* (.0036) SIZE*Tech users_rel-.0051* (.0013) SMALL*EMP_REGION.0120* (.0045) SIZE*EMP_REGION-.0069* (.0027) Observations105,902 Log Likelihood67,93767,96268,02567,95767,945

41. II. Effects of Regional Agglomeration Conditional on Plant Status Variable Name(1)(2)(4)(5) Tech Users in Related Ind.0355* (.0030).0280* (.0032).0360* (.0030).0297* (.0032) Regional Employment.0656* (.0087).0680* (.0088).0041* (.0088).0980* (.0156) Interaction Terms SINGLE* Tech users_rel.0214* (.0038).0161* (.0040) SINGLE*EMP_REGION.500* (.0081).405* (.0084) Observations105,902 Log Likelihood67,93767,97067,97667,993

42. Conclusion I. Technology adoption is facilitated by the industrial system that are characterized as follow: 1. 1.That are agglomerated with small plants 2. 2.That are agglomerated with single-unit plants 3. 3.That are agglomerated with plants that are similar II. II.Plants with the following Characteristics are more likely to benefit from the regional agglomeration and knowledge spillovers: 1. 1.Plants that are small 2. 2.Plants that are single-unit