What is he best known for?
The fields of study he is best known for:
- Law
- Microeconomics
- Finance
His scientific interests lie mostly in Microeconomics, Industrial organization, Competition, Information asymmetry and Monetary economics.
His Microeconomics research includes themes of Capital structure, Capital call and Opportunism.
While working on this project, Daniel F. Spulber studies both Industrial organization and Wellhead.
Daniel F. Spulber works mostly in the field of Competition, limiting it down to topics relating to Deregulation and, in certain cases, Returns to scale, Public policy, Game theory and Quality.
His Monetary economics study incorporates themes from Factor price and Market microstructure.
His Market microstructure study combines topics in areas such as Intermediary, Mainstream, Intermediation and Financial market.
His most cited work include:
- Menu Costs and the Neutrality of Money (491 citations)
- Business-to-Business Electronic Commerce (377 citations)
- Market Microstructure and Intermediation (310 citations)
What are the main themes of his work throughout his whole career to date?
Industrial organization, Microeconomics, Competition, Incentive and Law and economics are his primary areas of study.
His research in Industrial organization is mostly concerned with Theory of the firm.
Monopoly, Oligopoly, Production, Investment and Nonlinear pricing are subfields of Microeconomics in which his conducts study.
His research combines Monetary economics and Investment.
While the research belongs to areas of Competition, he spends his time largely on the problem of Deregulation, intersecting his research to questions surrounding Unbundling.
His work is dedicated to discovering how Law and economics, Damages are connected with Patent infringement and other disciplines.
He most often published in these fields:
- Industrial organization (32.31%)
- Microeconomics (23.85%)
- Competition (21.54%)
What were the highlights of his more recent work (between 2012-2021)?
- Industrial organization (32.31%)
- Incentive (14.23%)
- Intellectual property (7.31%)
In recent papers he was focusing on the following fields of study:
Daniel F. Spulber focuses on Industrial organization, Incentive, Intellectual property, Microeconomics and Competition.
His research in Industrial organization intersects with topics in Market power, Deregulation, Control, Creative destruction and Monopoly.
In his study, which falls under the umbrella issue of Incentive, Supply and demand and Monopolistic competition is strongly linked to Product market.
The study incorporates disciplines such as Tacit knowledge, Gains from trade, Liability and Misappropriation in addition to Intellectual property.
His Microeconomics research is multidisciplinary, incorporating elements of Neoclassical economics and Principal.
His studies deal with areas such as Government, Economic efficiency, Public economics and Public policy as well as Competition.
Between 2012 and 2021, his most popular works were:
- First-Party Content and Coordination in Two-Sided Markets (109 citations)
- How do competitive pressures affect incentives to innovate when there is a market for inventions (37 citations)
- The Innovative Entrepreneur (20 citations)
In his most recent research, the most cited papers focused on:
- Law
- Microeconomics
- Finance
Daniel F. Spulber mainly focuses on Industrial organization, Microeconomics, Intellectual property, Competition and Incentive.
He integrates Industrial organization with Information networks in his study.
Daniel F. Spulber is interested in Monopoly, which is a field of Microeconomics.
His Intellectual property study also includes
- Key which connect with Majority rule and Added value,
- Market power which is related to area like Adverse selection, Market microstructure, Search cost and Theory of the firm.
The various areas that Daniel F. Spulber examines in his Competition study include Deregulation and Dynamic efficiency.
His Incentive research incorporates elements of Transaction cost and Creative destruction.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
