Using MicroData for Policy Advice

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Using Micro-Data for Policy Advice

• Tobias Schmidt Christian Rammer
• Centre for European Economic Research (ZEW)
• Industrial Economics and International Management
  
• schmidt_at_zew.de

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Outline

• The ZEW
• Our Datasets and Their Usage
• Potential for Policy Advice
• Examples
• Conclusion

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The Centre for EuropeanEconomic Research (ZEW)

• Non-profit economic research institute
• Founded in 1990 by the Federal State of Baden
  Württemberg
• 50 institutional funding (Federal Gvt. Federal
  States), 50 contract research
• 130 employees, about 2/3 scientists
• 5 Departments 1 Research Group
• Labor Markets
• Corporate Taxation
• Industrial Economics
• Environmental Economics
• International Finance
• Information and Communication Technologies
• Contracts from Federal Ministries, EU, Companies,
  Foundations
• Microeconomics and Microeconometrics, Large
  Databases

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Databases - Industrial Economics

• Mannheim Innovation Panel / CIS
• Mannheim Enterprise Panel / Mannheim Foundation
  Panel
• Patent Data from the European Patent Office and
  German Patent and Trademark Office
• Database on Federal RD funding (firms public
  research organizations) ("PROFI")

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Usage of Databases A 3-Tiered Approach

• Data gathering, cleaning, expanding, merging gt
  High Quality Databases, up-to-date information
• Reporting to clients and policy advicegt
  Influence on policy makers through assessment of
  current state and identification of potential
  areas of concern
• Scientific analysisgt Detailed insight into
  determinants of and relationships between
  observed phenomena

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Reporting to Clients and Policy Advice

• Reports on key innovation indicators for
  manufacturing and services as well as individual
  industries (annually).
• In-depth report on innovation indicators with a
  focus on current policy issues (bi-annually).
• Figures and data for government and EU reports
  (e.g., Federal Report on Research Innovation
  in Europe Report on Germany's Technological
  Performance).
• Accompanying studies and papers on specific
  issues (e.g. Environmental Innovations,
  evaluation of RD funding).

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Scientific Analysis

• Microeconometric analysis of key scientific
  issues
• Determinants of innovation behavior and
  innovative success of firms
• Innovation and employment
• Framework conditions for innovation activities
  (hampering factors, IPR, public funding, etc.)
• Patent Behavior
• Lead Markets
• Policy Evaluation
• Matching Procedure
• Behavioral Additionally
• Scientific and Education Use Files

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Potential for Policy Advice

• Flexibility with respect to relevant policy
  topics through annual surveys and linking of
  different datasets.
• Comprehensive coverage of innovation activities
  in German firms and firm dynamics.
• Evaluation of public RD and innovation policies
  in particular funding of RD projects.
• International comparability through participation
  in EU projects (e.g. CIS).

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Example 1 Evaluation of RD funding

• Databases
• Mannheim Innovation Panel
• PROFI Database
• Questions analysed
• How effective (with respect to innovative
  success, patents) is public RD funding?
• Does RD funding crowd out or increase private
  RD spending?
• Is public funding more effective if it targets
  diffusion oriented projects or high-tech
  projects?
• Does funding favor larger over smaller firms?

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Example 2 Evaluation of public assistance
programs for young firms

• Databases
• Mannheim Foundation Panel
• Deutsche Ausgleichsbank (Dta - public
  SME-oriented bank) Database
• Questions analysed
• Does public assistance increase the life-time of
  newly founded firms?
• Does public assistance increase the average
  annual employment growth rates of young German
  firms?

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Example 3 Methods for Evaluation

• Databases
• Mannheim Enterprise Panel
• PROFI
• Mannheim Innovation Panel
• Goals
• Test possible applications of matching and
  selection-correction estimation techniques
  (previously used in labor market economics) for
  evaluating public RD funding
• Results
• Found to be suitable for evaluation of RD
  funding policy
• Use of matching techniques recommended in BMWA
  policy evaluation guidelines.

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Example 4 Knowledge and Technology Transfer

• Database
• Mannheim Innovation Panel
• Interviews, Case Studies
• Goals
• Analysis of knowledge and technology transfer
  between public research institutions and private
  firms (in particular SMEs).
• Documentation of current state and recommendation
  for technology policy.
• Identification of success factors and good
  practice for the set-up of technology transfer.

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Obstacles for Knowledge and Technology Transfer
(I)

Central question What are the obstacles to
Knowledge and Technology transfer
(KTT)? Hypothesis The perceived importance of
obstacles for KTT differs among different types
of public research institutes, because their
institutional framework is different. Method Prob
it estimations of the importance of 8 different
obstacles to KTT.
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Obstacles for Knowledge and Technology Transfer
(II)

• Obstacles for KTT included (dependent variables)
• Teaching burden high
• Lack of financial resources for KTT
• Lack of qualified personnel
• Lack of adequate technological equipment
• Cumbersome administrative procedures
• Lack of administrative support
• Lack of support with utilization of RD results
• Lack of openness of private firms
• Dummy variablesOne, if obstacle is highly or
  very important. Zero if it is at most of medium
  importance.

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Obstacles for Knowledge and Technology Transfer
(III)

Importance of Obstacles for KTT in Germany
between 1997-1999

Means of answers on a 6-point Likert scale 0 not
important at all, 5 highly important
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Obstacles for Knowledge and Technology Transfer
(IV)

• Factors influencing the perceived importance of
  obstacles for KTT (independent variables)
• Type of Institution (University, TU, WGL, )
• Structural Factors
• Field of science (physics, chemical, etc.)
• Basic or applied science
• Structure of employment (share of Ph.D.s, )
• Magnitude of third-party/external funds
• Size in terms of number of employees
• Situated in East or West Germany

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Results (Selection)


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Conclusion

• Micro-data is a valuable source for policy
  evaluation and tracking of government
  interventions.
• Policy advice through reports (documentation of
  status-quo) and in-depth scientific analysis.
• It is beneficial to combine different datasets
  for the analysis of a given topic.