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  • Browse content in A1 - General Economics
  • A10 - General
  • A12 - Relation of Economics to Other Disciplines
  • A13 - Relation of Economics to Social Values
  • A14 - Sociology of Economics
  • Browse content in A2 - Economic Education and Teaching of Economics
  • A29 - Other
  • Browse content in B - History of Economic Thought, Methodology, and Heterodox Approaches
  • B0 - General
  • Browse content in B1 - History of Economic Thought through 1925
  • B11 - Preclassical (Ancient, Medieval, Mercantilist, Physiocratic)
  • B12 - Classical (includes Adam Smith)
  • Browse content in B2 - History of Economic Thought since 1925
  • B20 - General
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  • B25 - Historical; Institutional; Evolutionary; Austrian
  • B26 - Financial Economics
  • Browse content in B3 - History of Economic Thought: Individuals
  • B31 - Individuals
  • Browse content in B4 - Economic Methodology
  • B41 - Economic Methodology
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  • B55 - Social Economics
  • Browse content in C - Mathematical and Quantitative Methods
  • Browse content in C0 - General
  • C00 - General
  • C02 - Mathematical Methods
  • Browse content in C1 - Econometric and Statistical Methods and Methodology: General
  • C10 - General
  • C11 - Bayesian Analysis: General
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  • C13 - Estimation: General
  • C14 - Semiparametric and Nonparametric Methods: General
  • C15 - Statistical Simulation Methods: General
  • C19 - Other
  • Browse content in C2 - Single Equation Models; Single Variables
  • C21 - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
  • C22 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
  • C23 - Panel Data Models; Spatio-temporal Models
  • C24 - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
  • C25 - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
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  • Browse content in C3 - Multiple or Simultaneous Equation Models; Multiple Variables
  • C31 - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
  • C32 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
  • C33 - Panel Data Models; Spatio-temporal Models
  • C34 - Truncated and Censored Models; Switching Regression Models
  • C35 - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
  • C36 - Instrumental Variables (IV) Estimation
  • Browse content in C4 - Econometric and Statistical Methods: Special Topics
  • C41 - Duration Analysis; Optimal Timing Strategies
  • C43 - Index Numbers and Aggregation
  • Browse content in C5 - Econometric Modeling
  • C51 - Model Construction and Estimation
  • C52 - Model Evaluation, Validation, and Selection
  • C53 - Forecasting and Prediction Methods; Simulation Methods
  • C54 - Quantitative Policy Modeling
  • Browse content in C6 - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
  • C60 - General
  • C61 - Optimization Techniques; Programming Models; Dynamic Analysis
  • C62 - Existence and Stability Conditions of Equilibrium
  • C63 - Computational Techniques; Simulation Modeling
  • Browse content in C7 - Game Theory and Bargaining Theory
  • C71 - Cooperative Games
  • C72 - Noncooperative Games
  • C73 - Stochastic and Dynamic Games; Evolutionary Games; Repeated Games
  • C78 - Bargaining Theory; Matching Theory
  • Browse content in C8 - Data Collection and Data Estimation Methodology; Computer Programs
  • C81 - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
  • C82 - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
  • C83 - Survey Methods; Sampling Methods
  • Browse content in C9 - Design of Experiments
  • C90 - General
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  • Browse content in D - Microeconomics
  • Browse content in D0 - General
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  • D01 - Microeconomic Behavior: Underlying Principles
  • D02 - Institutions: Design, Formation, Operations, and Impact
  • D03 - Behavioral Microeconomics: Underlying Principles
  • D04 - Microeconomic Policy: Formulation; Implementation, and Evaluation
  • Browse content in D1 - Household Behavior and Family Economics
  • D10 - General
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  • D12 - Consumer Economics: Empirical Analysis
  • D13 - Household Production and Intrahousehold Allocation
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  • D21 - Firm Behavior: Theory
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  • D23 - Organizational Behavior; Transaction Costs; Property Rights
  • D24 - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
  • D29 - Other
  • Browse content in D3 - Distribution
  • D30 - General
  • D31 - Personal Income, Wealth, and Their Distributions
  • D33 - Factor Income Distribution
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  • D40 - General
  • D41 - Perfect Competition
  • D43 - Oligopoly and Other Forms of Market Imperfection
  • D44 - Auctions
  • Browse content in D5 - General Equilibrium and Disequilibrium
  • D50 - General
  • D53 - Financial Markets
  • D58 - Computable and Other Applied General Equilibrium Models
  • Browse content in D6 - Welfare Economics
  • D60 - General
  • D61 - Allocative Efficiency; Cost-Benefit Analysis
  • D62 - Externalities
  • D63 - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
  • D64 - Altruism; Philanthropy
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  • Browse content in D7 - Analysis of Collective Decision-Making
  • D70 - General
  • D71 - Social Choice; Clubs; Committees; Associations
  • D72 - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
  • D73 - Bureaucracy; Administrative Processes in Public Organizations; Corruption
  • D74 - Conflict; Conflict Resolution; Alliances; Revolutions
  • D78 - Positive Analysis of Policy Formulation and Implementation
  • Browse content in D8 - Information, Knowledge, and Uncertainty
  • D80 - General
  • D81 - Criteria for Decision-Making under Risk and Uncertainty
  • D82 - Asymmetric and Private Information; Mechanism Design
  • D83 - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
  • D84 - Expectations; Speculations
  • D85 - Network Formation and Analysis: Theory
  • D86 - Economics of Contract: Theory
  • Browse content in D9 - Micro-Based Behavioral Economics
  • D90 - General
  • D91 - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
  • D92 - Intertemporal Firm Choice, Investment, Capacity, and Financing
  • Browse content in E - Macroeconomics and Monetary Economics
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  • E23 - Production
  • E24 - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
  • E25 - Aggregate Factor Income Distribution
  • E27 - Forecasting and Simulation: Models and Applications
  • Browse content in E3 - Prices, Business Fluctuations, and Cycles
  • E30 - General
  • E31 - Price Level; Inflation; Deflation
  • E32 - Business Fluctuations; Cycles
  • E37 - Forecasting and Simulation: Models and Applications
  • Browse content in E4 - Money and Interest Rates
  • E40 - General
  • E41 - Demand for Money
  • E42 - Monetary Systems; Standards; Regimes; Government and the Monetary System; Payment Systems
  • E43 - Interest Rates: Determination, Term Structure, and Effects
  • E44 - Financial Markets and the Macroeconomy
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  • Browse content in E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit
  • E50 - General
  • E51 - Money Supply; Credit; Money Multipliers
  • E52 - Monetary Policy
  • E58 - Central Banks and Their Policies
  • Browse content in E6 - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook
  • E60 - General
  • E61 - Policy Objectives; Policy Designs and Consistency; Policy Coordination
  • E62 - Fiscal Policy
  • E63 - Comparative or Joint Analysis of Fiscal and Monetary Policy; Stabilization; Treasury Policy
  • E65 - Studies of Particular Policy Episodes
  • E69 - Other
  • Browse content in E7 - Macro-Based Behavioral Economics
  • E70 - General
  • E71 - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on the Macro Economy
  • Browse content in F - International Economics
  • Browse content in F0 - General
  • F02 - International Economic Order and Integration
  • Browse content in F1 - Trade
  • F10 - General
  • F11 - Neoclassical Models of Trade
  • F12 - Models of Trade with Imperfect Competition and Scale Economies; Fragmentation
  • F13 - Trade Policy; International Trade Organizations
  • F14 - Empirical Studies of Trade
  • F15 - Economic Integration
  • F16 - Trade and Labor Market Interactions
  • F17 - Trade Forecasting and Simulation
  • F18 - Trade and Environment
  • Browse content in F2 - International Factor Movements and International Business
  • F21 - International Investment; Long-Term Capital Movements
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  • F23 - Multinational Firms; International Business
  • F24 - Remittances
  • Browse content in F3 - International Finance
  • F30 - General
  • F31 - Foreign Exchange
  • F32 - Current Account Adjustment; Short-Term Capital Movements
  • F33 - International Monetary Arrangements and Institutions
  • F34 - International Lending and Debt Problems
  • F35 - Foreign Aid
  • F36 - Financial Aspects of Economic Integration
  • F37 - International Finance Forecasting and Simulation: Models and Applications
  • Browse content in F4 - Macroeconomic Aspects of International Trade and Finance
  • F40 - General
  • F41 - Open Economy Macroeconomics
  • F42 - International Policy Coordination and Transmission
  • F43 - Economic Growth of Open Economies
  • F44 - International Business Cycles
  • F45 - Macroeconomic Issues of Monetary Unions
  • Browse content in F5 - International Relations, National Security, and International Political Economy
  • F50 - General
  • F51 - International Conflicts; Negotiations; Sanctions
  • F52 - National Security; Economic Nationalism
  • F53 - International Agreements and Observance; International Organizations
  • F55 - International Institutional Arrangements
  • F59 - Other
  • Browse content in F6 - Economic Impacts of Globalization
  • F62 - Macroeconomic Impacts
  • F63 - Economic Development
  • F64 - Environment
  • Browse content in G - Financial Economics
  • Browse content in G0 - General
  • G01 - Financial Crises
  • G02 - Behavioral Finance: Underlying Principles
  • Browse content in G1 - General Financial Markets
  • G10 - General
  • G11 - Portfolio Choice; Investment Decisions
  • G12 - Asset Pricing; Trading volume; Bond Interest Rates
  • G14 - Information and Market Efficiency; Event Studies; Insider Trading
  • G15 - International Financial Markets
  • G18 - Government Policy and Regulation
  • Browse content in G2 - Financial Institutions and Services
  • G20 - General
  • G21 - Banks; Depository Institutions; Micro Finance Institutions; Mortgages
  • G22 - Insurance; Insurance Companies; Actuarial Studies
  • G24 - Investment Banking; Venture Capital; Brokerage; Ratings and Ratings Agencies
  • G28 - Government Policy and Regulation
  • Browse content in G3 - Corporate Finance and Governance
  • G32 - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
  • G33 - Bankruptcy; Liquidation
  • G34 - Mergers; Acquisitions; Restructuring; Corporate Governance
  • G35 - Payout Policy
  • G38 - Government Policy and Regulation
  • Browse content in H - Public Economics
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  • Browse content in H1 - Structure and Scope of Government
  • H10 - General
  • H11 - Structure, Scope, and Performance of Government
  • H12 - Crisis Management
  • Browse content in H2 - Taxation, Subsidies, and Revenue
  • H20 - General
  • H21 - Efficiency; Optimal Taxation
  • H22 - Incidence
  • H23 - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
  • H24 - Personal Income and Other Nonbusiness Taxes and Subsidies; includes inheritance and gift taxes
  • H25 - Business Taxes and Subsidies
  • H26 - Tax Evasion and Avoidance
  • Browse content in H3 - Fiscal Policies and Behavior of Economic Agents
  • H30 - General
  • H31 - Household
  • Browse content in H4 - Publicly Provided Goods
  • H40 - General
  • H41 - Public Goods
  • H42 - Publicly Provided Private Goods
  • Browse content in H5 - National Government Expenditures and Related Policies
  • H50 - General
  • H51 - Government Expenditures and Health
  • H52 - Government Expenditures and Education
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  • H54 - Infrastructures; Other Public Investment and Capital Stock
  • H55 - Social Security and Public Pensions
  • H56 - National Security and War
  • Browse content in H6 - National Budget, Deficit, and Debt
  • H60 - General
  • H61 - Budget; Budget Systems
  • H62 - Deficit; Surplus
  • H63 - Debt; Debt Management; Sovereign Debt
  • Browse content in H7 - State and Local Government; Intergovernmental Relations
  • H70 - General
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  • H72 - State and Local Budget and Expenditures
  • H75 - State and Local Government: Health; Education; Welfare; Public Pensions
  • H76 - State and Local Government: Other Expenditure Categories
  • H77 - Intergovernmental Relations; Federalism; Secession
  • Browse content in H8 - Miscellaneous Issues
  • H83 - Public Administration; Public Sector Accounting and Audits
  • H84 - Disaster Aid
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  • Browse content in I - Health, Education, and Welfare
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  • Browse content in I1 - Health
  • I10 - General
  • I12 - Health Behavior
  • I14 - Health and Inequality
  • I15 - Health and Economic Development
  • I18 - Government Policy; Regulation; Public Health
  • I19 - Other
  • Browse content in I2 - Education and Research Institutions
  • I20 - General
  • I21 - Analysis of Education
  • I22 - Educational Finance; Financial Aid
  • I23 - Higher Education; Research Institutions
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  • I25 - Education and Economic Development
  • I26 - Returns to Education
  • I28 - Government Policy
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  • Browse content in I3 - Welfare, Well-Being, and Poverty
  • I30 - General
  • I31 - General Welfare
  • I32 - Measurement and Analysis of Poverty
  • I38 - Government Policy; Provision and Effects of Welfare Programs
  • Browse content in J - Labor and Demographic Economics
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  • J01 - Labor Economics: General
  • J08 - Labor Economics Policies
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  • J11 - Demographic Trends, Macroeconomic Effects, and Forecasts
  • J12 - Marriage; Marital Dissolution; Family Structure; Domestic Abuse
  • J13 - Fertility; Family Planning; Child Care; Children; Youth
  • J14 - Economics of the Elderly; Economics of the Handicapped; Non-Labor Market Discrimination
  • J15 - Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
  • J16 - Economics of Gender; Non-labor Discrimination
  • J17 - Value of Life; Forgone Income
  • J18 - Public Policy
  • Browse content in J2 - Demand and Supply of Labor
  • J20 - General
  • J21 - Labor Force and Employment, Size, and Structure
  • J22 - Time Allocation and Labor Supply
  • J23 - Labor Demand
  • J24 - Human Capital; Skills; Occupational Choice; Labor Productivity
  • J26 - Retirement; Retirement Policies
  • J28 - Safety; Job Satisfaction; Related Public Policy
  • Browse content in J3 - Wages, Compensation, and Labor Costs
  • J30 - General
  • J31 - Wage Level and Structure; Wage Differentials
  • J32 - Nonwage Labor Costs and Benefits; Retirement Plans; Private Pensions
  • J33 - Compensation Packages; Payment Methods
  • J38 - Public Policy
  • Browse content in J4 - Particular Labor Markets
  • J41 - Labor Contracts
  • J42 - Monopsony; Segmented Labor Markets
  • J45 - Public Sector Labor Markets
  • J46 - Informal Labor Markets
  • Browse content in J5 - Labor-Management Relations, Trade Unions, and Collective Bargaining
  • J50 - General
  • J51 - Trade Unions: Objectives, Structure, and Effects
  • J52 - Dispute Resolution: Strikes, Arbitration, and Mediation; Collective Bargaining
  • J53 - Labor-Management Relations; Industrial Jurisprudence
  • J54 - Producer Cooperatives; Labor Managed Firms; Employee Ownership
  • J58 - Public Policy
  • Browse content in J6 - Mobility, Unemployment, Vacancies, and Immigrant Workers
  • J60 - General
  • J61 - Geographic Labor Mobility; Immigrant Workers
  • J62 - Job, Occupational, and Intergenerational Mobility
  • J63 - Turnover; Vacancies; Layoffs
  • J64 - Unemployment: Models, Duration, Incidence, and Job Search
  • J65 - Unemployment Insurance; Severance Pay; Plant Closings
  • J68 - Public Policy
  • Browse content in J7 - Labor Discrimination
  • J71 - Discrimination
  • Browse content in J8 - Labor Standards: National and International
  • J81 - Working Conditions
  • J88 - Public Policy
  • Browse content in K - Law and Economics
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  • Browse content in K1 - Basic Areas of Law
  • K11 - Property Law
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  • K13 - Tort Law and Product Liability; Forensic Economics
  • K14 - Criminal Law
  • K16 - Election Law
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  • K31 - Labor Law
  • K32 - Environmental, Health, and Safety Law
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  • Browse content in K4 - Legal Procedure, the Legal System, and Illegal Behavior
  • K41 - Litigation Process
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  • Browse content in L - Industrial Organization
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  • L12 - Monopoly; Monopolization Strategies
  • L13 - Oligopoly and Other Imperfect Markets
  • L14 - Transactional Relationships; Contracts and Reputation; Networks
  • L16 - Industrial Organization and Macroeconomics: Industrial Structure and Structural Change; Industrial Price Indices
  • Browse content in L2 - Firm Objectives, Organization, and Behavior
  • L20 - General
  • L21 - Business Objectives of the Firm
  • L22 - Firm Organization and Market Structure
  • L23 - Organization of Production
  • L24 - Contracting Out; Joint Ventures; Technology Licensing
  • L25 - Firm Performance: Size, Diversification, and Scope
  • L26 - Entrepreneurship
  • L29 - Other
  • Browse content in L3 - Nonprofit Organizations and Public Enterprise
  • L30 - General
  • L31 - Nonprofit Institutions; NGOs; Social Entrepreneurship
  • L32 - Public Enterprises; Public-Private Enterprises
  • L33 - Comparison of Public and Private Enterprises and Nonprofit Institutions; Privatization; Contracting Out
  • Browse content in L4 - Antitrust Issues and Policies
  • L40 - General
  • L41 - Monopolization; Horizontal Anticompetitive Practices
  • L43 - Legal Monopolies and Regulation or Deregulation
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  • L50 - General
  • L51 - Economics of Regulation
  • L52 - Industrial Policy; Sectoral Planning Methods
  • L53 - Enterprise Policy
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  • L60 - General
  • L66 - Food; Beverages; Cosmetics; Tobacco; Wine and Spirits
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  • L71 - Mining, Extraction, and Refining: Hydrocarbon Fuels
  • L78 - Government Policy
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  • L81 - Retail and Wholesale Trade; e-Commerce
  • L83 - Sports; Gambling; Recreation; Tourism
  • L86 - Information and Internet Services; Computer Software
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  • L94 - Electric Utilities
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  • Browse content in M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
  • Browse content in M1 - Business Administration
  • M12 - Personnel Management; Executives; Executive Compensation
  • M14 - Corporate Culture; Social Responsibility
  • M16 - International Business Administration
  • Browse content in M3 - Marketing and Advertising
  • M31 - Marketing
  • Browse content in M5 - Personnel Economics
  • M50 - General
  • M51 - Firm Employment Decisions; Promotions
  • M52 - Compensation and Compensation Methods and Their Effects
  • M53 - Training
  • M54 - Labor Management
  • M55 - Labor Contracting Devices
  • Browse content in N - Economic History
  • Browse content in N1 - Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations
  • N10 - General, International, or Comparative
  • N11 - U.S.; Canada: Pre-1913
  • N12 - U.S.; Canada: 1913-
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  • N70 - General, International, or Comparative
  • N72 - U.S.; Canada: 1913-
  • Browse content in N9 - Regional and Urban History
  • N97 - Africa; Oceania
  • Browse content in O - Economic Development, Innovation, Technological Change, and Growth
  • Browse content in O1 - Economic Development
  • O10 - General
  • O11 - Macroeconomic Analyses of Economic Development
  • O12 - Microeconomic Analyses of Economic Development
  • O13 - Agriculture; Natural Resources; Energy; Environment; Other Primary Products
  • O14 - Industrialization; Manufacturing and Service Industries; Choice of Technology
  • O15 - Human Resources; Human Development; Income Distribution; Migration
  • O16 - Financial Markets; Saving and Capital Investment; Corporate Finance and Governance
  • O17 - Formal and Informal Sectors; Shadow Economy; Institutional Arrangements
  • O18 - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
  • O19 - International Linkages to Development; Role of International Organizations
  • Browse content in O2 - Development Planning and Policy
  • O22 - Project Analysis
  • O23 - Fiscal and Monetary Policy in Development
  • O24 - Trade Policy; Factor Movement Policy; Foreign Exchange Policy
  • O25 - Industrial Policy
  • Browse content in O3 - Innovation; Research and Development; Technological Change; Intellectual Property Rights
  • O30 - General
  • O31 - Innovation and Invention: Processes and Incentives
  • O32 - Management of Technological Innovation and R&D
  • O33 - Technological Change: Choices and Consequences; Diffusion Processes
  • O34 - Intellectual Property and Intellectual Capital
  • O38 - Government Policy
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  • Browse content in O4 - Economic Growth and Aggregate Productivity
  • O40 - General
  • O41 - One, Two, and Multisector Growth Models
  • O42 - Monetary Growth Models
  • O43 - Institutions and Growth
  • O47 - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
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  • Browse content in O5 - Economywide Country Studies
  • O50 - General
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  • O55 - Africa
  • O57 - Comparative Studies of Countries
  • Browse content in P - Economic Systems
  • Browse content in P1 - Capitalist Systems
  • P10 - General
  • P13 - Cooperative Enterprises
  • P16 - Political Economy
  • P17 - Performance and Prospects
  • Browse content in P2 - Socialist Systems and Transitional Economies
  • P20 - General
  • P26 - Political Economy; Property Rights
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  • P31 - Socialist Enterprises and Their Transitions
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  • P48 - Political Economy; Legal Institutions; Property Rights; Natural Resources; Energy; Environment; Regional Studies
  • Browse content in P5 - Comparative Economic Systems
  • P50 - General
  • Browse content in Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
  • Browse content in Q0 - General
  • Q02 - Commodity Markets
  • Browse content in Q1 - Agriculture
  • Q11 - Aggregate Supply and Demand Analysis; Prices
  • Q13 - Agricultural Markets and Marketing; Cooperatives; Agribusiness
  • Q15 - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment
  • Q16 - R&D; Agricultural Technology; Biofuels; Agricultural Extension Services
  • Q17 - Agriculture in International Trade
  • Q18 - Agricultural Policy; Food Policy
  • Browse content in Q2 - Renewable Resources and Conservation
  • Q20 - General
  • Q22 - Fishery; Aquaculture
  • Q23 - Forestry
  • Q25 - Water
  • Q26 - Recreational Aspects of Natural Resources
  • Q29 - Other
  • Browse content in Q3 - Nonrenewable Resources and Conservation
  • Q30 - General
  • Q32 - Exhaustible Resources and Economic Development
  • Q33 - Resource Booms
  • Q34 - Natural Resources and Domestic and International Conflicts
  • Q38 - Government Policy
  • Browse content in Q4 - Energy
  • Q40 - General
  • Q41 - Demand and Supply; Prices
  • Q42 - Alternative Energy Sources
  • Q43 - Energy and the Macroeconomy
  • Q48 - Government Policy
  • Browse content in Q5 - Environmental Economics
  • Q50 - General
  • Q51 - Valuation of Environmental Effects
  • Q52 - Pollution Control Adoption Costs; Distributional Effects; Employment Effects
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Methods Used in Economic Research: An Empirical Study of Trends and Levels

The methods used in economic research are analyzed on a sample of all 3,415 regular research papers published in 10 general interest journals every 5th year from 1997 to 2017. The papers are classified into three main groups by method: theory, experiments, and empirics. The theory and empirics groups are almost equally large. Most empiric papers use the classical method, which derives an operational model from theory and runs regressions. The number of papers published increases by 3.3% p.a. Two trends are highly significant: The fraction of theoretical papers has fallen by 26 pp (percentage points), while the fraction of papers using the classical method has increased by 15 pp. Economic theory predicts that such papers exaggerate, and the papers that have been analyzed by meta-analysis confirm the prediction. It is discussed if other methods have smaller problems.

1 Introduction

This paper studies the pattern in the research methods in economics by a sample of 3,415 regular papers published in the years 1997, 2002, 2007, 2012, and 2017 in 10 journals. The analysis builds on the beliefs that truth exists, but it is difficult to find, and that all the methods listed in the next paragraph have problems as discussed in Sections 2 and 4. Hereby I do not imply that all – or even most – papers have these problems, but we rarely know how serious it is when we read a paper. A key aspect of the problem is that a “perfect” study is very demanding and requires far too much space to report, especially if the paper looks for usable results. Thus, each paper is just one look at an aspect of the problem analyzed. Only when many studies using different methods reach a joint finding, we can trust that it is true.

Section 2 discusses the classification of papers by method into three main categories: (M1) Theory , with three subgroups: (M1.1) economic theory, (M1.2) statistical methods, and (M1.3) surveys. (M2) Experiments , with two subgroups: (M2.1) lab experiments and (M2.2) natural experiments. (M3) Empirics , with three subgroups: (M3.1) descriptive, (M3.2) classical empirics, and (M3.3) newer empirics. More than 90% of the papers are easy to classify, but a stochastic element enters in the classification of the rest. Thus, the study has some – hopefully random – measurement errors.

Section 3 discusses the sample of journals chosen. The choice has been limited by the following main criteria: It should be good journals below the top ten A-journals, i.e., my article covers B-journals, which are the journals where most research economists publish. It should be general interest journals, and the journals should be so different that it is likely that patterns that generalize across these journals apply to more (most?) journals. The Appendix gives some crude counts of researchers, departments, and journals. It assesses that there are about 150 B-level journals, but less than half meet the criteria, so I have selected about 15% of the possible ones. This is the most problematic element in the study. If the reader accepts my choice, the paper tells an interesting story about economic research.

All B-level journals try hard to have a serious refereeing process. If our selection is representative, the 150 journals have increased the annual number of papers published from about 7,500 in 1997 to about 14,000 papers in 2017, giving about 200,000 papers for the period. Thus, the B-level dominates our science. Our sample is about 6% for the years covered, but less than 2% of all papers published in B-journals in the period. However, it is a larger fraction of the papers in general interest journals.

It is impossible for anyone to read more than a small fraction of this flood of papers. Consequently, researchers compete for space in journals and for attention from the readers, as measured in the form of citations. It should be uncontroversial that papers that hold a clear message are easier to publish and get more citations. Thus, an element of sales promotion may enter papers in the form of exaggeration , which is a joint problem for all eight methods. This is in accordance with economic theory that predicts that rational researchers report exaggerated results; see Paldam ( 2016 , 2018 ). For empirical papers, meta-methods exist to summarize the results from many papers, notably papers using regressions. Section 4.4 reports that meta-studies find that exaggeration is common.

The empirical literature surveying the use of research methods is quite small, as I have found two articles only: Hamermesh ( 2013 ) covers 748 articles in 6 years a decade apart studies in three A-journals using a slightly different classification of methods, [1] while my study covers B-journals. Angrist, Azoulay, Ellison, Hill, and Lu ( 2017 ) use a machine-learning classification of 134,000 papers in 80 journals to look at the three main methods. My study subdivide the three categories into eight. The machine-learning algorithm is only sketched, so the paper is difficult to replicate, but it is surely a major effort. A key result in both articles is the strong decrease of theory in economic publications. This finding is confirmed, and it is shown that the corresponding increase in empirical articles is concentrated on the classical method.

I have tried to explain what I have done, so that everything is easy to replicate, in full or for one journal or one year. The coding of each article is available at least for the next five years. I should add that I have been in economic research for half a century. Some of the assessments in the paper will reflect my observations/experience during this period (indicated as my assessments). This especially applies to the judgements expressed in Section 4.

2 The eight categories

Table 1 reports that the annual number of papers in the ten journals has increased 1.9 times, or by 3.3% per year. The Appendix gives the full counts per category, journal, and year. By looking at data over two decades, I study how economic research develops. The increase in the production of papers is caused by two factors: The increase in the number of researchers. The increasing importance of publications for the careers of researchers.

The 3,415 papers

2.1 (M1) Theory: subgroups (M1.1) to (M1.3)

Table 2 lists the groups and main numbers discussed in the rest of the paper. Section 2.1 discusses (M1) theory. Section 2.2 covers (M2) experimental methods, while Section 2.3 looks at (M3) empirical methods using statistical inference from data.

The 3,415 papers – fractions in percent

The change of the fractions from 1997 to 2017 in percentage points

Note: Section 3.4 tests if the pattern observed in Table 3 is statistically significant. The Appendix reports the full data.

2.1.1 (M1.1) Economic theory

Papers are where the main content is the development of a theoretical model. The ideal theory paper presents a (simple) new model that recasts the way we look at something important. Such papers are rare and obtain large numbers of citations. Most theoretical papers present variants of known models and obtain few citations.

In a few papers, the analysis is verbal, but more than 95% rely on mathematics, though the technical level differs. Theory papers may start by a descriptive introduction giving the stylized fact the model explains, but the bulk of the paper is the formal analysis, building a model and deriving proofs of some propositions from the model. It is often demonstrated how the model works by a set of simulations, including a calibration made to look realistic. However, the calibrations differ greatly by the efforts made to reach realism. Often, the simulations are in lieu of an analytical solution or just an illustration suggesting the magnitudes of the results reached.

Theoretical papers suffer from the problem known as T-hacking , [2] where the able author by a careful selection of assumptions can tailor the theory to give the results desired. Thus, the proofs made from the model may represent the ability and preferences of the researcher rather than the properties of the economy.

2.1.2 (M1.2) Statistical method

Papers reporting new estimators and tests are published in a handful of specialized journals in econometrics and mathematical statistics – such journals are not included. In our general interest journals, some papers compare estimators on actual data sets. If the demonstration of a methodological improvement is the main feature of the paper, it belongs to (M1.2), but if the economic interpretation is the main point of the paper, it belongs to (M3.2) or (M3.3). [3]

Some papers, including a special issue of Empirical Economics (vol. 53–1), deal with forecasting models. Such models normally have a weak relation to economic theory. They are sometimes justified precisely because of their eclectic nature. They are classified as either (M1.2) or (M3.1), depending upon the focus. It appears that different methods work better on different data sets, and perhaps a trade-off exists between the user-friendliness of the model and the improvement reached.

2.1.3 (M1.3) Surveys

When the literature in a certain field becomes substantial, it normally presents a motley picture with an amazing variation, especially when different schools exist in the field. Thus, a survey is needed, and our sample contains 68 survey articles. They are of two types, where the second type is still rare:

2.1.3.1 (M1.3.1) Assessed surveys

Here, the author reads the papers and assesses what the most reliable results are. Such assessments require judgement that is often quite difficult to distinguish from priors, even for the author of the survey.

2.1.3.2 (M1.3.2) Meta-studies

They are quantitative surveys of estimates of parameters claimed to be the same. Over the two decades from 1997 to 2017, about 500 meta-studies have been made in economics. Our sample includes five, which is 0.15%. [4] Meta-analysis has two levels: The basic level collects and codes the estimates and studies their distribution. This is a rather objective exercise where results seem to replicate rather well. [5] The second level analyzes the variation between the results. This is less objective. The papers analyzed by meta-studies are empirical studies using method (M3.2), though a few use estimates from (M3.1) and (M3.3).

2.2 (M2) Experimental methods: subgroups (M2.1) and (M2.2)

Experiments are of three distinct types, where the last two are rare, so they are lumped together. They are taking place in real life.

2.2.1 (M2.1) Lab experiments

The sample had 1.9% papers using this method in 1997, and it has expanded to 9.7% in 2017. It is a technique that is much easier to apply to micro- than to macroeconomics, so it has spread unequally in the 10 journals, and many experiments are reported in a couple of special journals that are not included in our sample.

Most of these experiments take place in a laboratory, where the subjects communicate with a computer, giving a controlled, but artificial, environment. [6] A number of subjects are told a (more or less abstract) story and paid to react in either of a number of possible ways. A great deal of ingenuity has gone into the construction of such experiments and in the methods used to analyze the results. Lab experiments do allow studies of behavior that are hard to analyze in any other way, and they frequently show sides of human behavior that are difficult to rationalize by economic theory. It appears that such demonstration is a strong argument for the publication of a study.

However, everything is artificial – even the payment. In some cases, the stories told are so elaborate and abstract that framing must be a substantial risk; [7] see Levitt and List ( 2007 ) for a lucid summary, and Bergh and Wichardt ( 2018 ) for a striking example. In addition, experiments cost money, which limits the number of subjects. It is also worth pointing to the difference between expressive and real behavior. It is typically much cheaper for the subject to “express” nice behavior in a lab than to be nice in the real world.

(M2.2) Event studies are studies of real world experiments. They are of two types:

(M2.2.1) Field experiments analyze cases where some people get a certain treatment and others do not. The “gold standard” for such experiments is double blind random sampling, where everything (but the result!) is preannounced; see Christensen and Miguel ( 2018 ). Experiments with humans require permission from the relevant authorities, and the experiment takes time too. In the process, things may happen that compromise the strict rules of the standard. [8] Controlled experiments are expensive, as they require a team of researchers. Our sample of papers contains no study that fulfills the gold standard requirements, but there are a few less stringent studies of real life experiments.

(M2.2.2) Natural experiments take advantage of a discontinuity in the environment, i.e., the period before and after an (unpredicted) change of a law, an earthquake, etc. Methods have been developed to find the effect of the discontinuity. Often, such studies look like (M3.2) classical studies with many controls that may or may not belong. Thus, the problems discussed under (M3.2) will also apply.

2.3 (M3) Empirical methods: subgroups (M3.1) to (M3.3)

The remaining methods are studies making inference from “real” data, which are data samples where the researcher chooses the sample, but has no control over the data generating process.

(M3.1) Descriptive studies are deductive. The researcher describes the data aiming at finding structures that tell a story, which can be interpreted. The findings may call for a formal test. If one clean test follows from the description, [9] the paper is classified under (M3.1). If a more elaborate regression analysis is used, it is classified as (M3.2). Descriptive studies often contain a great deal of theory.

Some descriptive studies present a new data set developed by the author to analyze a debated issue. In these cases, it is often possible to make a clean test, so to the extent that biases sneak in, they are hidden in the details of the assessments made when the data are compiled.

(M3.2) Classical empirics has three steps: It starts by a theory, which is developed into an operational model. Then it presents the data set, and finally it runs regressions.

The significance levels of the t -ratios on the coefficient estimated assume that the regression is the first meeting of the estimation model and the data. We all know that this is rarely the case; see also point (m1) in Section 4.4. In practice, the classical method is often just a presentation technique. The great virtue of the method is that it can be applied to real problems outside academia. The relevance comes with a price: The method is quite flexible as many choices have to be made, and they often give different results. Preferences and interests, as discussed in Sections 4.3 and 4.4 below, notably as point (m2), may affect these choices.

(M3.3) Newer empirics . Partly as a reaction to the problems of (M3.2), the last 3–4 decades have seen a whole set of newer empirical techniques. [10] They include different types of VARs, Bayesian techniques, causality/co-integration tests, Kalman Filters, hazard functions, etc. I have found 162 (or 4.7%) papers where these techniques are the main ones used. The fraction was highest in 1997. Since then it has varied, but with no trend.

I think that the main reason for the lack of success for the new empirics is that it is quite bulky to report a careful set of co-integration tests or VARs, and they often show results that are far from useful in the sense that they are unclear and difficult to interpret. With some introduction and discussion, there is not much space left in the article. Therefore, we are dealing with a cookbook that makes for rather dull dishes, which are difficult to sell in the market.

Note the contrast between (M3.2) and (M3.3): (M3.2) makes it possible to write papers that are too good, while (M3.3) often makes them too dull. This contributes to explain why (M3.2) is getting (even) more popular and the lack of success of (M3.3), but then, it is arguable that it is more dangerous to act on exaggerated results than on results that are weak.

3 The 10 journals

The 10 journals chosen are: (J1) Can [Canadian Journal of Economics], (J2) Emp [Empirical Economics], (J3) EER [European Economic Review], (J4) EJPE [European Journal of Political Economy], (J5) JEBO [Journal of Economic Behavior & Organization], (J6) Inter [Journal of International Economics], (J7) Macro [Journal of Macroeconomics], (J8) Kyklos, (J9) PuCh [Public Choice], and (J10) SJE [Scandinavian Journal of Economics].

Section 3.1 discusses the choice of journals, while Section 3.2 considers how journals deal with the pressure for publication. Section 3.3 shows the marked difference in publication profile of the journals, and Section 3.4 tests if the trends in methods are significant.

3.1 The selection of journals

They should be general interest journals – methodological journals are excluded. By general interest, I mean that they bring papers where an executive summary may interest policymakers and people in general. (ii) They should be journals in English (the Canadian Journal includes one paper in French), which are open to researchers from all countries, so that the majority of the authors are from outside the country of the journal. [11] (iii) They should be sufficiently different so that it is likely that patterns, which apply to these journals, tell a believable story about economic research. Note that (i) and (iii) require some compromises, as is evident in the choice of (J2), (J6), (J7), and (J8) ( Table 4 ).

The 10 journals covered

Note. Growth is the average annual growth from 1997 to 2017 in the number of papers published.

Methodological journals are excluded, as they are not interesting to outsiders. However, new methods are developed to be used in general interest journals. From studies of citations, we know that useful methodological papers are highly cited. If they remain unused, we presume that it is because they are useless, though, of course, there may be a long lag.

The choice of journals may contain some subjectivity, but I think that they are sufficiently diverse so that patterns that generalize across these journals will also generalize across a broader range of good journals.

The papers included are the regular research articles. Consequently, I exclude short notes to other papers and book reviews, [12] except for a few article-long discussions of controversial books.

3.2 Creating space in journals

As mentioned in the introduction, the annual production of research papers in economics has now reached about 1,000 papers in top journals, and about 14,000 papers in the group of good journals. [13] The production has grown with 3.3% per year, and thus it has doubled the last twenty years. The hard-working researcher will read less than 100 papers a year. I know of no signs that this number is increasing. Thus, the upward trend in publication must be due to the large increase in the importance of publications for the careers of researchers, which has greatly increased the production of papers. There has also been a large increase in the number of researches, but as citations are increasingly skewed toward the top journals (see Heckman & Moktan, 2018 ), it has not increased demand for papers correspondingly. The pressures from the supply side have caused journals to look for ways to create space.

Book reviews have dropped to less than 1/3. Perhaps, it also indicates that economists read fewer books than they used to. Journals have increasingly come to use smaller fonts and larger pages, allowing more words per page. The journals from North-Holland Elsevier have managed to cram almost two old pages into one new one. [14] This makes it easier to publish papers, while they become harder to read.

Many journals have changed their numbering system for the annual issues, making it less transparent how much they publish. Only three – Canadian Economic Journal, Kyklos, and Scandinavian Journal of Economics – have kept the schedule of publishing one volume of four issues per year. It gives about 40 papers per year. Public Choice has a (fairly) consistent system with four volumes of two double issues per year – this gives about 100 papers. The remaining journals have changed their numbering system and increased the number of papers published per year – often dramatically.

Thus, I assess the wave of publications is caused by the increased supply of papers and not to the demand for reading material. Consequently, the study confirms and updates the observation by Temple ( 1918 , p. 242): “… as the world gets older the more people are inclined to write but the less they are inclined to read.”

3.3 How different are the journals?

The appendix reports the counts for each year and journal of the research methods. From these counts, a set of χ 2 -scores is calculated for the three main groups of methods – they are reported in Table 5 . It gives the χ 2 -test comparing the profile of each journal to the one of the other nine journals taken to be the theoretical distribution.

The methodological profile of the journals –  χ 2 -scores for main groups

Note: The χ 2 -scores are calculated relative to all other journals. The sign (+) or (−) indicates if the journal has too many or too few papers relatively in the category. The P -values for the χ 2 (3)-test always reject that the journal has the same methodological profile as the other nine journals.

The test rejects that the distribution is the same as the average for any of the journals. The closest to the average is the EJPE and Public Choice. The two most deviating scores are for the most micro-oriented journal JEBO, which brings many experimental papers, and of course, Empirical Economics, which brings many empirical papers.

3.4 Trends in the use of the methods

Table 3 already gave an impression of the main trends in the methods preferred by economists. I now test if these impressions are statistically significant. The tests have to be tailored to disregard three differences between the journals: their methodological profiles, the number of papers they publish, and the trend in the number. Table 6 reports a set of distribution free tests, which overcome these differences. The tests are done on the shares of each research method for each journal. As the data cover five years, it gives 10 pairs of years to compare. [15] The three trend-scores in the []-brackets count how often the shares go up, down, or stay the same in the 10 cases. This is the count done for a Kendall rank correlation comparing the five shares with a positive trend (such as 1, 2, 3, 4, and 5).

Trend-scores and tests for the eight subgroups of methods across the 10 journals

Note: The three trend-scores in each [ I 1 , I 2 , I 3 ]-bracket are a Kendall-count over all 10 combinations of years. I 1 counts how often the share goes up. I 2 counts when the share goes down, and I 3 counts the number of ties. Most ties occur when there are no observations either year. Thus, I 1 + I 2 + I 3 = 10. The tests are two-sided binominal tests disregarding the zeroes. The test results in bold are significant at the 5% level.

The first set of trend-scores for (M1.1) and (J1) is [1, 9, 0]. It means that 1 of the 10 share-pairs increases, while nine decrease and no ties are found. The two-sided binominal test is 2%, so it is unlikely to happen. Nine of the ten journals in the (M1.1)-column have a majority of falling shares. The important point is that the counts in one column can be added – as is done in the all-row; this gives a powerful trend test that disregards differences between journals and the number of papers published. ( Table A1 )

Four of the trend-tests are significant: The fall in theoretical papers and the rise in classical papers. There is also a rise in the share of stat method and event studies. It is surprising that there is no trend in the number of experimental studies, but see Table A2 (in Appendix).

4 An attempt to interpret the pattern found

The development in the methods pursued by researchers in economics is a reaction to the demand and supply forces on the market for economic papers. As already argued, it seems that a key factor is the increasing production of papers.

The shares add to 100, so the decline of one method means that the others rise. Section 4.1 looks at the biggest change – the reduction in theory papers. Section 4.2 discusses the rise in two new categories. Section 4.3 considers the large increase in the classical method, while Section 4.4 looks at what we know about that method from meta-analysis.

4.1 The decline of theory: economics suffers from theory fatigue [16]

The papers in economic theory have dropped from 59.5 to 33.6% – this is the largest change for any of the eight subgroups. [17] It is highly significant in the trend test. I attribute this drop to theory fatigue.

As mentioned in Section 2.1, the ideal theory paper presents a (simple) new model that recasts the way we look at something important. However, most theory papers are less exciting: They start from the standard model and argue that a well-known conclusion reached from the model hinges upon a debatable assumption – if it changes, so does the conclusion. Such papers are useful. From a literature on one main model, the profession learns its strengths and weaknesses. It appears that no generally accepted method exists to summarize this knowledge in a systematic way, though many thoughtful summaries have appeared.

I think that there is a deeper problem explaining theory fatigue. It is that many theoretical papers are quite unconvincing. Granted that the calculations are done right, believability hinges on the realism of the assumptions at the start and of the results presented at the end. In order for a model to convince, it should (at least) demonstrate the realism of either the assumptions or the outcome. [18] If both ends appear to hang in the air, it becomes a game giving little new knowledge about the world, however skillfully played.

The theory fatigue has caused a demand for simulations demonstrating that the models can mimic something in the world. Kydland and Prescott pioneered calibration methods (see their 1991 ). Calibrations may be carefully done, but it often appears like a numerical solution of a model that is too complex to allow an analytical solution.

4.2 Two examples of waves: one that is still rising and another that is fizzling out

When a new method of gaining insights in the economy first appears, it is surrounded by doubts, but it also promises a high marginal productivity of knowledge. Gradually the doubts subside, and many researchers enter the field. After some time this will cause the marginal productivity of the method to fall, and it becomes less interesting. The eight methods include two newer ones: Lab experiments and newer stats. [19]

It is not surprising that papers with lab experiments are increasing, though it did take a long time: The seminal paper presenting the technique was Smith ( 1962 ), but only a handful of papers are from the 1960s. Charles Plott organized the first experimental lab 10 years later – this created a new standard for experiments, but required an investment in a lab and some staff. Labs became more common in the 1990s as PCs got cheaper and software was developed to handle experiments, but only 1.9% of the papers in the 10 journals reported lab experiments in 1997. This has now increased to 9.7%, so the wave is still rising. The trend in experiments is concentrated in a few journals, so the trend test in Table 6 is insignificant, but it is significant in the Appendix Table A2 , where it is done on the sum of articles irrespective of the journal.

In addition to the rising share of lab experiment papers in some journals, the journal Experimental Economics was started in 1998, where it published 281 pages in three issues. In 2017, it had reached 1,006 pages in four issues, [20] which is an annual increase of 6.5%.

Compared with the success of experimental economics, the motley category of newer empirics has had a more modest success, as the fraction of papers in the 5 years are 5.8, 5.2, 3.5, 5.4, and 4.2, which has no trend. Newer stats also require investment, but mainly in human capital. [21] Some of the papers using the classical methodology contain a table with Dickey-Fuller tests or some eigenvalues of the data matrix, but they are normally peripheral to the analysis. A couple of papers use Kalman filters, and a dozen papers use Bayesian VARs. However, it is clear that the newer empirics have made little headway into our sample of general interest journals.

4.3 The steady rise of the classical method: flexibility rewarded

The typical classical paper provides estimates of a key effect that decision-makers outside academia want to know. This makes the paper policy relevant right from the start, and in many cases, it is possible to write a one page executive summary to the said decision-makers.

The three-step convention (see Section 2.3) is often followed rather loosely. The estimation model is nearly always much simpler than the theory. Thus, while the model can be derived from a theory, the reverse does not apply. Sometimes, the model seems to follow straight from common sense, and if the link from the theory to the model is thin, it begs the question: Is the theory really necessary? In such cases, it is hard to be convinced that the tests “confirm” the theory, but then, of course, tests only say that the data do not reject the theory.

The classical method is often only a presentation devise. Think of a researcher who has reached a nice publishable result through a long and tortuous path, including some failed attempts to find such results. It is not possible to describe that path within the severely limited space of an article. In addition, such a presentation would be rather dull to read, and none of us likes to talk about wasted efforts that in hindsight seem a bit silly. Here, the classical method becomes a convenient presentation device.

The biggest source of variation in the results is the choice of control/modifier variables. All datasets presumably contain some general and some special information, where the latter depends on the circumstances prevailing when the data were compiled. The regression should be controlled for these circumstances in order to reach the general result. Such ceteris paribus controls are not part of the theory, so many possible controls may be added. The ones chosen for publication often appear to be the ones delivering the “right” results by the priors of the researcher. The justification for their inclusion is often thin, and if two-stage regressions are used, the first stage instruments often have an even thinner justification.

Thus, the classical method is rather malleable to the preferences and interests of researchers and sponsors. This means that some papers using the classical technique are not what they pretend, as already pointed out by Leamer ( 1983 ), see also Paldam ( 2018 ) for new references and theory. The fact that data mining is tempting suggests that it is often possible to reach smashing results, making the paper nice to read. This may be precisely why it is cited.

Many papers using the classical method throw in some bits of exotic statistics technique to demonstrate the robustness of the result and the ability of the researcher. This presumably helps to generate credibility.

4.4 Knowledge about classical papers reached from meta-studies

Individual studies using the classical method often look better than they are, and thus they are more uncertain than they appear, but we may think of the value of convergence for large N s (number of observations) as the truth. The exaggeration is largest in the beginning of a new literature, but gradually it becomes smaller. Thus, the classical method does generate truth when the effect searched for has been studied from many sides. The word research does mean that the search has to be repeated! It is highly risky to trust a few papers only.

Meta-analysis has found other results such as: Results in top journals do not stand out. It is necessary to look at many journals, as many papers on the same effect are needed. Little of the large variation between results is due to the choice of estimators.

A similar development should occur also for experimental economics. Experiments fall in families: A large number cover prisoner’s dilemma games, but there are also many studies of dictator games, auction games, etc. Surveys summarizing what we have learned about these games seem highly needed. Assessed summaries of old experiments are common, notably in introductions to papers reporting new ones. It should be possible to extract the knowledge reached by sets of related lab experiments in a quantitative way, by some sort of meta-technique, but this has barely started. The first pioneering meta-studies of lab experiments do find the usual wide variation of results from seemingly closely related experiments. [25] A recent large-scale replicability study by Camerer et al. ( 2018 ) finds that published experiments in the high quality journal Nature and Science exaggerate by a factor two just like regression studies using the classical method.

5 Conclusion

The study presents evidence that over the last 20 years economic research has moved away from theory towards empirical work using the classical method.

From the eighties onward, there has been a steady stream of papers pointing out that the classical method suffers from excess flexibility. It does deliver relevant results, but they tend to be too good. [26] While, increasingly, we know the size of the problems of the classical method, systematic knowledge about the problems of the other methods is weaker. It is possible that the problems are smaller, but we do not know.

Therefore, it is clear that obtaining solid knowledge about the size of an important effect requires a great deal of papers analyzing many aspects of the effect and a careful quantitative survey. It is a well-known principle in the harder sciences that results need repeated independent replication to be truly trustworthy. In economics, this is only accepted in principle.

The classical method of empirical research is gradually winning, and this is a fine development: It does give answers to important policy questions. These answers are highly variable and often exaggerated, but through the efforts of many competing researchers, solid knowledge will gradually emerge.

Home page: http://www.martin.paldam.dk

Acknowledgments

The paper has been presented at the 2018 MAER-Net Colloquium in Melbourne, the Kiel Aarhus workshop in 2018, and at the European Public Choice 2019 Meeting in Jerusalem. I am grateful for all comments, especially from Chris Doucouliagos, Eelke de Jong, and Bob Reed. In addition, I thank the referees for constructive advice.

Conflict of interest: Author states no conflict of interest.

Appendix: Two tables and some assessments of the size of the profession

The text needs some numbers to assess the representativity of the results reached. These numbers just need to be orders of magnitude. I use the standard three-level classification in A, B, and C of researchers, departments, and journals. The connections between the three categories are dynamic and rely on complex sorting mechanisms. In an international setting, it matters that researchers have preferences for countries, notably their own. The relation between the three categories has a stochastic element.

The World of Learning organization reports on 36,000 universities, colleges, and other institutes of tertiary education and research. Many of these institutions are mainly engaged in undergraduate teaching, and some are quite modest. If half of these institutions have a program in economics, with a staff of at least five, the total stock of academic economists is 100,000, of which most are at the C-level.

The A-level of about 500 tenured researchers working at the top ten universities (mainly) publishes in the top 10 journals that bring less than 1,000 papers per year; [27] see Heckman and Moktan (2020). They (mainly) cite each other, but they greatly influence other researchers. [28] The B-level consists of about 15–20,000 researchers who work at 4–500 research universities, with graduate programs and ambitions to publish. They (mainly) publish in the next level of about 150 journals. [29] In addition, there are at least another 1,000 institutions that strive to move up in the hierarchy.

The counts for each of the 10 journals

Counts, shares, and changes for all ten journals for subgroups

Note: The trend-scores are calculated as in Table 6 . Compared to the results in Table 6 , the results are similar, but the power is less than before. However, note that the results in Column (M2.1) dealing with experiments are stronger in Table A2 . This has to do with the way missing observations are treated in the test.

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The power of ‘geoeconomics’ to make sense of a turbulent world

research paper about economy

For thousands of years, powerful countries have used economic tools as carrots and sticks to get others to do what they want. The Romans, Medicis, French, and British all did it during their respective rules. Today, the United States and China are using economic coercion on multiple fronts — including the U.S.-led sanctions against Russia over its war on Ukraine and China’s massive funding of power plants, airports, and other infrastructure projects around the world.

As global tensions rise, one scholar shedding new light on the phenomenon known as “geoeconomics” is Matteo Maggiori , the Moghadam Family Professor of Finance at the Stanford Graduate School of Business, senior fellow at the Stanford Institute for Economic Policy Research ( SIEPR ), and co-founder of The Global Capital Allocation Project . In a new paper , Maggiori maps out how countries wield geoeconomic power to achieve their goals, details the impact of this power on the world at large, and applies these results to current policies in the U.S. and China. Here, Maggiori talks about his insights and how they can help policymakers, business leaders, and the general public to better understand 21st century geoeconomics.

Geoeconomics has been described as an emerging area within economics and yet the concept is not new. What’s going on?

We define geoeconomic power as the ability of governments to use their countries’ economic strength from existing financial and trade relationships to achieve geopolitical and economic goals. After World War II, given the importance of geoeconomic power in reshaping the world economy, this was a big focus among economists. But while the study of geoeconomic power remained active in political science and international relations, it soon became dormant in economic research, partly due to the lack of the theoretical tools to properly characterize the complexities involved.

Today, competition between the U.S. and China has made geoeconomics once again very salient. And we, as economists, now have more tools to take a serious crack at understanding what geoeconomic power is, where it comes from, and what are the likely impacts of wielding it. We’re able to combine elements from economic theory, macroeconomics, and the literature on networks, industrial policy, and trade to provide more clarity for policymakers and business leaders trying to understand the dynamics at hand.

How do you, along with your co-authors Jesse Schreger at Columbia and Chris Clayton at Yale, help shed light on this phenomenon?

We’ve developed a framework that explains how geoeconomic power arises from the ability of countries like China and the United States to induce behaviors by other governments or firms when legal contracts are incomplete and the high costs of a military operation aren’t justified.

Countries with geoeconomic power are able to leverage their multiple economic relationships — arising, for example, from lending or access to manufacturing resources — in their favor. They can pressure governments and firms to undertake costly actions — actions that frequently take the form of tariffs, markups of goods, surcharges on loans, or import-export related restrictions on industry sectors or countries. They can also threaten retaliatory moves — either directly or indirectly — against a deviating entity, thereby increasing their power to compel other governments or firms to honor their contractual obligations or do something that benefits the powerful country. We provide a theory of the joint optimal use of these strategic instruments.

Where have we recently seen geoeconomic maneuvers?

Our paper describes in detail two examples of modern geoeconomic power. One is China’s flagship Belt and Road Initiative. China provides emerging market countries with package deals of lending, infrastructure, and manufacturing support in exchange for, among other things, political concessions, such as closer alignment over the recognition of Taiwan.

The other example is the U.S. demand to European countries to stop using 5G technology supplied by China’s Huawei because of national security concerns. We explain why and how the U.S., by exerting pressure on a subset of Huawei customers, was able to indirectly get countries and firms it couldn’t influence directly to also stop using the company’s technology.

Both examples illustrate the underlying mechanism: The implicit or explicit threats used by geoeconomic powers like China and the U.S. work because each country has control over multiple resources that others need but can’t easily access elsewhere. By controlling the supply of various goods and services, powerful countries can manipulate the world economy in their favor.

Is having geoeconomics power good or bad for the world in general?

In our framework, geoeconomic power can have an overall positive effect. China and the U.S. are able to generate more economic activity in a world where contract enforceability can be a real challenge. Think, for instance, of the construction of mines or airports in developing nations. However, geoeconomic powers also extract a lot of surplus — or economic benefits — from that increased activity, and how much of that surplus they take for themselves versus how much is left for everybody else is an open question.

Your paper also addresses the limits of geoeconomic power.

There are limits whenever there are alternative buyers and sellers of the goods or services that a country is using as a threat. The U.S.-led refusal to buy oil and gas from Russia once it invaded Ukraine would have had a bigger impact if China and India had been part of the coalition; instead, its effect was more limited because Russia still had alternative markets to sell its energy products.

Another example is the global financial system. The US controls a large part of the world financial payment and settlement systems. Threats to shut off the access to these systems can be very powerful because there currently are limited alternatives. However, these threats also create incentives for other countries to create alternative systems. China, for example, has been attempting to create its own payment system with an eye to decreasing the US power in this dimension.

There is also a natural limit to what you can ask for before people will no longer want to deal with you. Countries like the U.S. and China can only wield their power if other entities find their demands worthwhile to comply with. 

What do you hope your framework inspires?

More research that helps improve policy on geoeconomics. I think we are at the beginning of a process similar to what happened during and subsequent to the financial crisis of 2008 and 2009, when policy debates around how to respond focused on extremes initially. Only over time were we able to make serious progress in terms of developing financial stability tools and understanding related trade-offs.

Today, the conversation around international economic policy often features polarized views: some advocate for completely unregulated trade and financial systems, while others would like the U.S. (or China) government to use its power for frequent and large interventions. My goal is to move people toward a middle ground where we converge on a set of rationales that can guide governments in wielding their geoeconomic power. Frameworks like ours can lead to more specific policy tools and criteria for limited and beneficial intervention. I hope that our work is a step in that direction.

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The impact of entrepreneurship on economic, social and environmental welfare and its determinants: a systematic review

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  • Volume 71 , pages 553–584, ( 2021 )

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This paper presents a systematic review of (a) the impact of entrepreneurship on economic, social and environmental welfare and (b) the factors determining this impact. Research over the past 25 years shows that entrepreneurship is one cause of macroeconomic development, but that the relationship between entrepreneurship and welfare is very complex. The literature emphasizes that the generally positive impact of entrepreneurship depends on a variety of associated determinants which affect the degree of this impact. This paper seeks to contribute to the literature in three ways. First, it updates and extends existing literature reviews with the recently emerged research stream on developing countries, and incorporates studies analysing not only the impact of entrepreneurship on economic growth and welfare but also on social and environmental welfare. Second, it identifies and structures the current knowledge on the determinants of this impact. And third, it provides a roadmap for future research which targets the shortcomings of the existing empirical literature on this topic. The review of 102 publications reveals that the literature generally lacks research which (a) goes beyond the common measures of economic welfare, (b) examines the long-term impact of entrepreneurship and (c) focuses on emerging and developing countries. Regarding the determinants of the impact of entrepreneurship, the results highlight the need for empirical research which addresses both already investigated determinants which require more attention (e.g. survival, internationalisation, qualifications) and those which are currently only suspected of shaping the impact of entrepreneurship (e.g. firm performance, the entrepreneur’s socio-cultural background and motivations).

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The Likely Determinants of Social Entrepreneurship and Policy Implications

Conceptualizing social entrepreneurship in the context of emerging economies: an integrative review of past research from briics.

Subhanjan Sengupta, Arunaditya Sahay & Francesca Croce

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1 Introduction

Entrepreneurship and its possible impact on the economy have been studied extensively during the past two decades but the research field still continues to develop and grow. The majority of studies from a variety of scientific disciplines have found empirical evidence for a significant positive macroeconomic impact of entrepreneurship (e.g. Atems and Shand 2018 ; Audretsch and Keilbach 2004a ; Fritsch and Mueller 2004 , 2008 ). However, several empirical studies show that the macroeconomic impact of entrepreneurship can also be negative under certain conditions (e.g. Carree and Thurik 2008 ; Andersson and Noseleit 2011 ; Fritsch and Mueller 2004 , 2008 ). Potential explanations for these contradictory results are to be found in the complex relationship between entrepreneurship and economic growth. Already some of the very first empirical studies on the macroeconomic impact of entrepreneurship showed that factors such as industrial affiliation (Fritsch 1996 ), the country’s level of development and the local density of business owners (Carree et al. 2002 ) significantly determine the impact of entrepreneurship. With more entrepreneurship datasets becoming available, researchers found evidence that only a small number of new firms such as particularly innovative new firms and firms with high-growth expectations create economic value and initiate Schumpeter’s process of ‘creative destruction’ (e.g. Szerb et al. 2018 ; Valliere and Peterson 2009 ; van Oort and Bosma 2013 ; Wong et al. 2005 ). However, over the past decade, researchers have identified a multitude of other relevant determinants (e.g. survival rates of new firms, institutional and cultural settings, motivations and qualifications of the entrepreneur), thereby drawing an increasingly complex web of interrelated determinants around the macroeconomic impact of entrepreneurship. This complexity combined with the fact that the research on determinants is scattered and mostly based on separate analyses of determinants leads to a number of hitherto unidentified research opportunities. In order to detect these opportunities and to exploit them in a targeted manner, a structured overview of the current knowledge on the determinants of the macroeconomic impact of entrepreneurship is required. In this context, a structured overview is not only essential for the scientific entrepreneurship community but also for politicians all over the world who need detailed information on the impact of entrepreneurship to promote the right types of entrepreneurship in the right situations.

To ensure that this information prepared for policy makers are truly comprehensive, it is essential that state-of-the-art research considers not only economic outcomes of entrepreneurship but also its social and environmental effects. This demand for a more holistic impact analyses is based on the call of economists who have been emphasizing since the 1970’s that economic development may is a significant part of welfare, but that social and environmental dimensions need to be considered as well (Daly et al. 1994 ; Meadows et al. 1972 ; Nordhaus and Tobin 1972 ). Tietenberg and Lewis ( 2012 , p. 553) summarised the economic, social and environmental effects in a holistic welfare definition and state that a “true measure of development would increase whenever we, as a nation or as a world, were better off and decrease whenever we were worse off”. This understatement is in line with many authors who recently highlighted the importance of entrepreneurship for social and environmental welfare (e.g. Alvarez and Barney 2014 ; Dhahri and Omri 2018 ; McMullen 2011 ). Entrepreneurship research has come to see entrepreneurs as a solution for social inequality and environmental degradation rather than a possible cause of them (Gast et al. 2017 ; Munoz and Cohen 2018 ; Terán-Yépez et al. 2020 ). This scientific consent of the past 50 years clearly illustrates how important it is that econometric research on entrepreneurship incorporates research on the economic as well as on the social and environmental impact of entrepreneurship. Footnote 1

Considering that the research on the macroeconomic impacts of entrepreneurship has been gaining increasing recognition over the last two decades and across a wide range of disciplines (Urbano et al. 2019a ), literature reviews must be conducted periodically to synthesize and reflect recent progress and to stimulate future research. Several high-quality reviews have already summarized the significant amount of research on the impact of entrepreneurship on the economy. Wennekers and Thurik ( 1999 ) were the first who discussed the link between entrepreneurship and economic growth in a narrative literature analysis. With their summary of the theoretical knowledge of that time and the first framework of the entrepreneurial impact the authors laid the groundwork for the following decade of empirical research on that matter. van Praag and Versloot ( 2007 ), extended that first review by systematically reviewing and evaluating the empirical findings of 57 articles published between 1995 and 2007. More precisely, the authors evaluated the various economic contributions of entrepreneurial firms, which have been defined by the authors as either employing fewer than 100 employees, being younger than 7 years or being new entrants into the market, relative to their counterparts. van Praag and Versloot ( 2007 ) thus made the first systematic attempt to distinguish the few new firms which are of economic relevance from the majority of meaningless new firms. Fritsch ( 2013 ), in a non-systematic monograph, exhaustively surveyed and assessed the then available knowledge on how new firms particularly effect regional development over time. Within this review, the author has established the term ‘determinants’ in the field of research on the impact of entrepreneurship and developed first suggestions on which factors may determine the impact of new firms. However, the author has not provided any empirical evidence for the effect of his proposed determinants. In contrast to these three literature reviews, the three most recent reviews also incorporated the latest findings from international studies and on developing countries. However, the three latest reviews all have a narrowly defined research focus. While Block et al. ( 2017 ; systematic literature review of 102 studies published between 2000 and 2015) analysed antecedents, behaviour and consequences of innovative entrepreneurship, Bjørnskov and Foss ( 2016 ; systematic literature review of 28 studies) and Urbano et al. ( 2019a ; systematic literature review of 104 studies published between 1992 and 2016) focused on the relationship between the institutional context, entrepreneurship and economic growth. Accordingly, all the existing reviews are either (1) already outdated, (2) mostly on highly developed countries or (3) focused on specific topics. Furthermore, none of these reviews provided (4) a structured overview on the empirical knowledge on the impact of entrepreneurship on the economy or (5) included research on the social and environmental impact of entrepreneurship.

This paper addresses these five shortcomings through a comprehensive and systematic review of empirical research into the impact of entrepreneurship on economic, Footnote 2 social and environmental welfare. The methodology of the review is based on the current knowledge of systematic reviews (e.g. Fayolle and Wright 2014 ; Fisch and Block 2018 ; Jones and Gatrell 2014 ; Tranfield et al. 2003 ), on narrative synthesis (e.g. Dixon-Woods et al. 2005 ; Jones and Gatrell 2014 ; Popay et al. 2006 ) and on recent examples of best practice (e.g. Jones et al. 2011 ; Urbano et al. 2019a ; van Praag and Versloot 2007 ). Using this approach, this paper aims to contribute to the literature on the impact of entrepreneurship on welfare in three ways. First, it updates and extends the existing literature reviews. More specifically, it follows recent research recommendations (e.g. Block et al. 2017 ; Fritsch 2013 ; Urbano et al. 2019a ) by incorporating the recent empirical stream of research on the impact of entrepreneurship in developing countries and research that goes beyond measures of common economic welfare. In practical terms, this means that this review not only considers measures of economic welfare (e.g. GDP, employment rates, innovative capacity), but also for social welfare (e.g. life expectancy, literacy rates, income inequality), for environmental welfare (e.g. CO 2 emissions, water pollution, soil quality) and for indicators which incorporate all three welfare dimensions (e.g. Index of Sustainable Economic Welfare, Genuine Progress Indicator). Second, this paper, as demanded in previous reviews (Fritsch 2013 ; Urbano et al. 2019a ), aims to provide a descriptive analysis of the factors determining the entrepreneurial impact by critically assessing (a) which determinants of the entrepreneurial impact have (b) what impact on (c) which measures of economic welfare. This paper thus represents the first comprehensive attempt to summarize and structure the empirical knowledge on the determinants of the impact of entrepreneurship. Finally, to encourage future research, this paper indicates shortcomings in the empirical research not only on the impact of entrepreneurship on economic, social and environmental welfare, but also on the described and structured determinants of this impact. It concludes with suggestions for future research avenues to close these research gaps.

To achieve these objectives, this paper is structured as follows. Section  2 describes the methodological approach of the review. Sections  3.1 and 3.2 report the available empirical research into the impact of entrepreneurship on economic, social and environmental welfare. Section  3.3 summarizes the determinants of this impact and Sect.  4 presents a roadmap for future research. Section  5 discusses the limitations of this paper and provides a conclusion.

2 Methodology

In order to clarify not only the macroeconomic impact of entrepreneurship on economic welfare but also the determinants of this impact, this paper provides a broad-ranging systematic, evidence-based literature review including a narrative synthesis. According to Mulrow ( 1994 ), systematic reviews are particularly useful in identifying and evaluating a large volume of evidence published over a long period of time and have been frequently applied in recent state-of-the-art literature reviews (e.g. Li et al. 2020 ; Mochkabadi and Volkmann 2020 ; Urbano et al. 2019a ). The systematic literature review conducted in this paper employs a rather broad empirical definition of entrepreneurship which covers both the entrepreneur, who creates or discovers new businesses (Kirzner 1973 ; Schumpeter 1942 ) and the entrepreneurial firm itself. Entrepreneurship is understood here as new business activity, which includes entrepreneurs in the process of new firm creation as well as recently founded firms. Furthermore, although not necessarily associated with the formation of new firms, self-employed individuals and owner-managers are defined here as entrepreneurs as well. This general definition is consistent with the majority of empirical studies (e.g. Bosma et al. 2011 ; Fritsch and Schindele 2011 ; Mueller et al. 2008 ). The review process comprises three major steps, namely (1) data collection, (2) the selection of relevant studies and (3) data synthesis.

2.1 Data collection

As a first step, to reduce bias and maintain objectivity in all stages of the review, a review panel was set up. The panel consists of the author, a professor and two doctoral students knowledgeable in this field of research. In order to obtain the most relevant terms for the systematic search, the suggestions of Tranfield et al. ( 2003 ) were followed and a number of scoping studies based on combinations of keywords related to the topic were performed. The insights from this initial search phase were used to further develop relevant search terms resulting in the Boolean search string presented in the online appendix. The number of selected search terms was intentionally rather broad to avoid overlooking potentially valuable studies. It included the most common terms and measures of entrepreneurship and of economic, social and environmental welfare. This search string was subsequently used to scan titles, abstracts, and enclosed keywords of studies in the electronic databases EBSCO Business Source Complete, ProQuest ABI/INFORM Global and Web of Science. These databases were selected, because they allow the application of complex search strings and cover an extensive range of scientific journals from a variety of different disciplines. In order to provide a quality threshold, only peer-reviewed journal articles were scanned, since they are considered as validated knowledge (Podsakoff et al. 2005 ; Ordanini et al. 2008 ). Unpublished papers, books, book chapters, conference papers and dissertations were omitted in the initial search. Furthermore, the search was restricted to studies written in English. The main search was conducted in May 2019 and updated once in December 2019. It yielded, after the removal of duplicates, an initial data set of n = 7533 studies.

In addition to the main search, three more steps were conducted to create an exhaustive sample. First, five journals of particular relevance for the discussion were manually searched. Footnote 3 Second, meta-studies and literature reviews on related topics were screened for additional studies. Footnote 4 And finally, based on the guidelines of Wohlin ( 2014 ), an iterative back- and forward snowballing approach was conducted. The whole process of data collection and selection and its results are summarized in Fig.  1 .

figure 1

Systematic process of data collection and selection

2.2 Data selection and quality assessment

The studies collected during the main search were carefully reviewed to determine whether they were suitable for the objective of this paper. Titles, abstracts and, in doubtful cases, whole studies were checked against the following set of selection criteria.

Studies must analyse the macroeconomic impact of entrepreneurship by applying at least one economic, social or environmental welfare measure on an aggregated regional, national or global level.

Studies must employ definitions of entrepreneurship as discussed in the introduction of Sect.  2 . Studies that solely analysed the impact of small firms, intrapreneurship, corporate-entrepreneurship, institutional entrepreneurship, or entrepreneurial capital were excluded.

Studies must apply adequate quantitative methods to measure the impact of entrepreneurship. Studies that only discuss this matter theoretically, that follow a qualitative approach or that do not go beyond simple correlation techniques were excluded.

Studies must analyse spatial units, as they seem to be considerably better suited to analysing the impact of entrepreneurship (Fritsch 2013 ). Studies that are based on the analysis of industry units were excluded.

Studies must analyse long-term panel data or data on an adequately aggregated level to account for demographic, political and economic events. Studies that analysed single spatial units over a short period of time were excluded.

Due to the broadness of the search string, the main search yielded many studies which solely dealt with the microeconomic performance of new firms or which analyse how the local level of development determines the number of new firms. Studies which were not related to the research questions or did not meet all five selection criteria, were manually removed. This process of selection in the main search led to a total of n = 92 studies. The three additional search steps increased this number by n = 10, resulting in a final data set of n = 102 studies, including two high-quality book chapters which present empirical results of particular relevance to the paper’s objective (namely Stam et al. 2011 ; Verheul and van Stel 2010 ). When comparing the sample size with that of related literature reviews, it appears to be appropriate. Hence, even if the selected sample is not exhaustive, it is very likely to be representative of the relevant literature.

2.3 Data analysis

Given that research in this area employs a variety of measures of entrepreneurship and of economic welfare and is methodologically diverse, it was unfeasible to perform a meta-analysis. Instead, an integrative and evidence-driven narrative synthesis based on the guidelines established by Popay et al. ( 2006 ) was chosen to aggregate, combine and summarise the diverse set of studies. Narrative synthesis is considered particularly useful when, as in this case, research area is characterised by heterogeneous methods, samples, theories, etc. (Fayolle and Wright 2014 ).

Once the final set of studies had been identified, the characteristics and study findings were extracted by carefully reading the methods and results sections. To reduce research bias, a review-specific data-extraction form was employed. The extraction-form is based on the suggestions of Tranfield et al. ( 2003 ) and Higgins and Green ( 2008 ) and contains general information, details about the analysed samples, the applied measures of entrepreneurship and economic welfare, the applied econometric techniques as well as short summaries of the relevant findings and the identified microeconomic impact factors.

3 Results of the literature review

The main results of the literature review regarding the impact of entrepreneurship on economic, social and environmental welfare and the determinants of this impact are presented in Table 5 (see online appendix). The large number of gathered studies on impact of entrepreneurship (n = 102) as well as on its determinants (n = 51) attest to the fact that this field of research has already been studied in great detail. Most of the identified studies were published in high-quality management, economics, social science and environmental science journals. Table  1 illustrates that the main part of the cross-disciplinary scientific discussion, however, took place in the Journals Small Business Economics (24%) and Regional Studies (7%). The number of empirical studies published per year has increased over the last decade, indicating the topicality of the research field and the need for an updated review of the new knowledge.

Figure  2 summarizes the statistics of the large amount of data gathered in Table 5 (see appendix) and illustrates the complexity of the research field. The left-hand-side lists the measures of entrepreneurship used in the analysed studies and shows how often they were applied. The most frequently applied measure of entrepreneurship is new firm formations either (a) per work force (labour market approach), (b) per number of existing firms (ecological approach) or (c) per capita. Another frequently applied measure of entrepreneurship is total early-stage entrepreneurial activity (TEA) based on data from the Global Entrepreneurship Monitor (Reynolds et al. 2003 ) or its subgroups: necessity-driven entrepreneurial activity (NEA), opportunity-driven entrepreneurial activity (OEA), innovative entrepreneurial activity (IEA) and high-growth expectation entrepreneurial activity (HEA). Other authors estimated regional entrepreneurship using self-employment or business ownership rates. The Kauffman Foundation Index for entrepreneurial activity is used less frequently, as it is a specific measure of entrepreneurship for US regions.

figure 2

Overview of applied measures of entrepreneurship and welfare, and analysed determinants. Note : the numbers in brackets represent the numbers of associated empirical studies

Regarding the right-hand-side of Fig.  2 , it is noticeable that the majority of authors analysed the impact of entrepreneurship on economic welfare, primarily on GDP, growth and employment-related measures. Far fewer studies analysed the impact on the economic measures of national competitiveness or innovativeness, e.g. the number of patent applications. In contrast to the clear research focus on economic welfare, only five studies were found which analysed the impact of entrepreneurship on environmental or social welfare. Although many common measures of social and environmental welfare (e.g. crime rates or ecological footprint) were explicitly included in the search string (see online Appendix), no studies could be found that analyse the impact of entrepreneurship on them.

Independent of the measures of entrepreneurship and welfare used, the reviewed studies test their relationship by applying a very heterogenous set of methods. With the availability of more and more cross-sectional data covering longer and high-frequency time-series, authors started to apply new econometric approaches such as pooled and panel data regressions, fixed effect models, and subsequently, dynamic panel data models. Most authors based their analyses on rather straightforward regression techniques.

Sections  3.1 and 3.2 discuss empirical knowledge relating to the impact of entrepreneurship on economic welfare as well as on social and environmental welfare. Section  3.3 deals with the empirical evidence on the factors which determine this impact of entrepreneurship (see the lower part of Fig.  2 ).

3.1 Impact of entrepreneurship on economic welfare

The analysed literature predominantly confirms the results of previous literature reviews and gives empirical evidence that new firm formations have a generally positive effect on regional development and economic performance. The relationship holds for all tested measures of entrepreneurship and is robust across a broad range of spatial and cultural contexts.

The impact does, however, differ over time. Fritsch and Mueller ( 2004 ) studied the time-lag structure of the impact of entrepreneurship by applying an Almon lag model of different polynomial orders in their study of 326 West German regions. Their results revealed that the impact of entrepreneurship follows a typical time-sequence: an S- or wave-shaped pattern which can be structured into three phases. Phase I is defined by a positive immediate increase of employment (direct effects of new capacities). After approximately 1 year, in phase II, this positive short-term impact becomes smaller, insignificant or even negative (displacement effects and market selection). Around year five, this medium-term impact becomes positive again and reaches a peak in year eight (supply-side and spill-over effects). This positive long-term effect of entrepreneurship on employment, which defines phase III, diminishes after a period of 10 years.

Table  2 presents the findings of all reviewed studies which analysed the impact of new firm formations on employment and GDP in one, two or all three phases. It shows that the findings regarding the impact of entrepreneurship on employment are largely consistent with the wave-pattern theory. The existence of the wave-pattern could be confirmed on different regional levels for Great Britain (Mueller et al. 2008 ), for the United States (Acs and Mueller 2008 ; Henderson and Weiler 2009 ), for Portugal (Baptista et al. 2008 ; Baptista and Preto 2010 , 2011 ), for West Germany (Fritsch and Mueller 2008 ; Fritsch and Noseleit 2013a ), for the Netherlands (van Stel and Suddle 2008 ; Koster 2011 ; Delfmann and Koster 2016 ), for Sweden (Andersson and Noseleit 2011 ), for China (Rho and Gao 2012 ) for Canada (Matejovsky et al. 2014 ) as well as in several cross-country studies on OECD countries (Audretsch et al. 2015 ; Carree and Thurik 2008 ; Koellinger and Thurik 2012 ; Thurik et al. 2008 ). Furthermore, the reviewed studies reveal that this relationship not only holds for new firm formations as a measure of entrepreneurship but also for self-employment (e.g. Matejovsky et al. 2014 ; Rho and Gao 2012 ; Thurik et al. 2008 ) and business ownership (e.g. Carree and Thurik 2008 ; Henderson and Weiler 2009 ; Koellinger and Thurik 2012 ). The latter two measures of entrepreneurship, however, seem to have a less pronounced impact (Acs and Armington 2004 ; Rho and Gao 2012 ; Dvouletý 2017 ). Empirical evidence suggests a similar wave-pattern for the impact of entrepreneurship on GDP. Studies on GDP analysing all three phases confirm the positive short- and long-term peaks. However, in contrast to the results on employment, they find the medium-term impact to be less pronounced and positive (Audretsch et al. 2015 ; Carree and Thurik 2008 ; Koellinger and Thurik 2012 ; Matejovsky et al. 2014 ). The few empirical results displayed in Table  2 , which contradict the wave-pattern theory (e.g. findings of a negative short-term impact of entrepreneurship on GDP), can largely be explained by certain determining factors such as a differing impact in developing countries (see Sect.  3.3.4 ) or of necessity-driven entrepreneurship (see Sect.  3.3.9 ).

The results for other measures of economic welfare are scarce and contradictory. Ferreira et al. ( 2017 ) analysed the short-term impact of entrepreneurship on different measures of competitiveness and found that TEA and IEA positively related to competitiveness. However, they found no significant relationship between OEA and competitiveness. On the contrary, a study by Mrozewski and Kratzer ( 2017 ) found a positive relationship between OEA and competitiveness, but not between TEA and competitiveness.

The empirical results regarding the impact of entrepreneurship on innovativeness are also inconclusive. Acs and Varga ( 2005 ) and Draghici and Albulescu ( 2014 ) found that OEA has a positive impact on patent applications and innovation indices, but that TEA and NEA do not have any significant impact on them. Anokhin and Wincent ( 2012 ) found a positive impact of TEA on innovativeness but a more recent study from Albulescu and Draghici ( 2016 ) found that neither TEA nor OEA have a significant relationship to innovativeness. Similarly, Cumming et al. ( 2014 ) found new firm formations based on the labour market approach have a positive short-term impact on patent applications, but new firm formations based on the ecological approach and business ownership rates do not.

3.2 The impact of entrepreneurship on social and environmental welfare

Contrary to the well-researched impact of entrepreneurship on employment and GDP, little is known about the impact on social and environmental welfare. Three independent studies recently found empirical evidence that entrepreneurship positively affects measures of social welfare. Rupasingha and Goetz ( 2013 ) found that in the short-term self-employment reduces poverty in rural and urban U.S. counties, Atems and Shand ( 2018 ) found that in the medium-term self-employment decreases income inequality in U.S. states and, finally, Dhahri and Omri ( 2018 ) found new firm formations to increase the national modified Human Development Index (MHDI) in developing countries.

The empirical research on the impact of new firm formations on environmental welfare, however, illustrates that entrepreneurship may also come with major drawbacks. Omri ( 2017 ) as well as Dhahri and Omri ( 2018 ) and Ben Youssef et al. ( 2018 ) found that new firms significantly increase the amount of national CO 2 -emissions. According to Ben Youssef et al. ( 2018 ), this unfortunate impact on CO 2 -emissions is in fact so great that, despite the positive impact on GDP, new firms decrease Genuine Savings (also known as adjusted net saving) in African countries. They also found that the impact is more pronounced for informal new firm formations. This finding matches the results of Omri ( 2017 ), who detected the impact on CO 2 -emissions to be lower in developed countries which generally have lower rates of informal entrepreneurship (Williams and Lansky 2013 ). Furthermore, Omri ( 2017 ) discovered that the relationship between new firm formations and CO 2 -emissions is not linear but can be described as exhibiting an inverted U-shape. Thus, at an already high level of entrepreneurship, new firm formations may result in a decrease in CO 2 -emissions.

3.3 Determinants of the impact of entrepreneurship

So far, the empirical results suggest, in many cases, a clear causal macroeconomic impact of new firm formations on economic measures of welfare. However, this topic is reasonably complex, and the complexity increases further when determining factors of this impact are considered. The lower part of Fig.  2 presents an overview of the empirical knowledge on these determinants. A key finding of this review, namely that all of the found analyses of determinants focus exclusively on the economic effects of entrepreneurship, is, however, not illustrated in Fig.  2 . The review revealed that, although they are strongly interdependent, the determinants of the impact of entrepreneurship can generally be categorized into external environmental conditions, firm level characteristics and individual characteristics of the entrepreneurs themselves. Figure  2 illustrates that most empirical research has been conducted on the determining environmental conditions and on the firm level characteristic innovativeness and on the individual level characteristic motivations . In fact, some of the determinants presented have already been thoroughly investigated in highly recommendable earlier literature reviews, namely: industry affiliation (Fritsch 2013 ), regional population - and entrepreneurship density (Fritsch 2013 ), institutions and culture (Bjørnskov and Foss 2016 ; Urbano et al. 2019a ), innovativeness (Block et al. 2017 ). The review for this paper confirms these findings and briefly summarizes the key learnings in the Sects. 3.3.1 to 3.3.3 and 3.3.5 . However, except for a recently emerged empirical research stream on innovativeness , no new insights could be gained on the already reviewed determinants. Therefore, the focus of this section is primarily on the empirical evidence which has not yet been systematically investigated.

3.3.1 Industry affiliation

Fritsch ( 1996 ) was one of the first to analyse how entrepreneurial impact differs between industries. He focused on the impact of new firm formations on employment in West Germany and found it to be significantly higher in the manufacturing sector than in the service sector. Several authors confirmed this finding for the Netherlands (van Stel and Suddle 2008 ), for West-Germany (Fritsch and Mueller 2004 ) and for Sweden (Andersson and Noseleit 2011 ). Other studies, however, found the impact of new firms on economic welfare measures to be higher in the service sector (Bosma et al. 2011 ; Koster and van Stel 2014 ). Fritsch ( 2013 ) reasoned that these contradicting results may be due to considerable differences between the industries in different regions or countries and thus an analysis at the industry level might be not appropriate at all. For more information on the industrial perspective of the entrepreneurial impact on the economy, Fritsch ( 2013 ) provides a comprehensive overview including policy implications and avenues for further research.

3.3.2 Regional population- and entrepreneurship density

In a second wave of literature, researchers analysed how the impact of entrepreneurship differs between regions. They found clear evidence that the magnitude of the entrepreneurial impact is positively related to the population density (Baptista and Preto 2011 ; Fritsch and Mueller 2004 , 2008 ; Fritsch and Schroeter 2011 ; Henderson and Weiler 2009 ; Lee 2017 ; Li et al. 2011 ; van Stel and Suddle 2008 ). In urban regions and agglomerations, new firms have a more pronounced and more positive impact on employment (Baptista and Preto 2011 ; Henderson and Weiler 2009 ; van Stel and Suddle 2008 ) and GDP (Audretsch et al. 2015 ; Belitski and Desai 2016 ) throughout all three previously described phases (see Sect.  3.1 ). On the contrary, in rural and less agglomerated regions, the entrepreneurial impact is weak and often negative (Fritsch and Mueller 2004 , 2008 ).

While the economic relevance of new firm formations seems to increase with the population density, empirical evidence suggests that this is not the case for the relation between firm formations and regional entrepreneurship density. On the contrary, several authors found that the economic effect of another new firm becomes lower the more entrepreneurs are already on the market and even zero for regions with high entrepreneurship rates close to equilibrium rate (e.g. Carree et al. 2002 , 2007 ; Mueller et al. 2008 ). These empirical insights identify entrepreneurship as a regional phenomenon and illustrate that macroeconomic effects of new firms are shaped by local conditions. An in-depth discussion of regional differences in the macroeconomic impact of new firms can be found in the monograph by Fritsch ( 2013 ).

3.3.3 Institutions and culture

To shed light on the complex interactions between institutions, entrepreneurship and economic growth, Urbano et al. ( 2019a ) and Bjørnskov and Foss ( 2016 ) recently conducted thorough literature reviews. The empirical evidence identified in the present paper (Aparicio et al. 2016 ; Audretsch and Keilbach 2004a , b , c ; Bjørnskov and Foss 2016 ) is in line with the findings of these two reviews which suggest that institutions affect the economy indirectly through endogenous factors like entrepreneurship. This holds true for formal institutions like (academic) support systems for new firms, procedures and costs to create a business, property rights or political structures as well as for informal institutions like social norms, cultures or belief systems (Urbano et al. 2019a ). However, in contrast to Bjørnskov and Foss ( 2016 ), Urbano et al. ( 2019a ) suggest that formal and informal institutions are not of equal importance, but that social norms and cultures have higher and more positive effects on the relation between entrepreneurship and economic growth.

3.3.4 Local level of development

While Sect.  3.1 illustrates that the impact of entrepreneurship in developed countries follows a typical wave-pattern, until now, no studies have analysed this time-pattern in developing countries. In general, the empirical evidence on the impact in developing countries is contradictory: some studies found a positive impact of entrepreneurship (Ben Youssef et al. 2018 ; Dhahri and Omri 2018 ; Feki and Mnif 2016 ; Stam et al. 2011 ), others found no or even a negative impact (Anokhin and Wincent, 2012 ; Ferreira et al. 2017 ; Verheul and van Stel 2010 ). However, studies which compared countries in different development stages found that the magnitude of the impact of entrepreneurship depends on the national welfare level and is generally higher in more developed countries (Anokhin and Wincent 2012 ; Carree et al. 2002 , 2007 ; Crnogaj et al. 2015 ; Hessels and van Stel 2011 ; Urbano and Aparicio 2016 ; Valliere and Peterson 2009 ; van Stel et al. 2005 ; Verheul and van Stel 2010 ). Furthermore, little is known on the mechanisms behind the impact of entrepreneurship in developing countries. Most of the few studies which specifically deal with developing countries (n = 19) analysed the impact on a national level (n = 16) based on GEM data (n = 12), focused on the impact on GDP related measures (n = 17), or solely analysed the short- or medium-term impact (n = 16).

3.3.5 Innovativeness

According to the knowledge spillover theory of entrepreneurship, new knowledge results in business opportunities and entrepreneurs exploit these opportunities by turning the new knowledge into innovative products (Acs et al. 2009 , 2013 ; Audretsch and Keilbach 2005 ). Recent studies confirm this theory and provide empirical evidence that entrepreneurship moderates the transformation of new knowledge into innovations (Block et al. 2013 ) and that innovative regions with higher levels of entrepreneurship perform economically better (González-Pernía et al. 2012 ). Accordingly, it is reasonable to assume that particularly innovative new firms are more important to economic welfare than their non-innovative counterparts. These considerations coincide with those presented in the literature review on innovative entrepreneurship by Block et al. ( 2017 ). However, the present systematic literature review extends the review of Block et al. ( 2017 ) by including previously unconsidered as well as recently emerged empirical evidence on the macroeconomic impact of innovative entrepreneurship. The identified empirical studies do indeed confirm the presumed positive impact of innovativeness. Crnogaj et al. ( 2015 ) as well as Du and O’Connor ( 2017 ) and Szerb et al. ( 2018 ) used GEM data to compare the impact of founders who stated their products or services to be new or at least unfamiliar to their customers. All of the previously mentioned authors found that innovative founders have a higher impact on GDP, economic efficiency, gross value added (GVA) and employment than less innovative founders. Furthermore, earlier studies attest to new firms which are in innovative, knowledge- or technology-intensive industries a higher than average impact on both GDP (Audretsch and Keilbach 2004a , b , 2005 , Mueller 2007 ) and employment (Baptista and Preto 2010 , 2011 ).

3.3.6 Firm survival

Empirical evidence suggests that a particularly important determinant of the impact of entrepreneurship is whether new firms are able to survive the first years. Falck ( 2007 ) was the first to find empirical evidence of a positive relationship between new firms which survive for at least 5 years and efficiency of the industry in which they are in. On the contrary, he could not find any significant relationship to industry level efficiency growth for firms which did not survive the first 5 years. Brixy ( 2014 ), Fritsch and Noseleit ( 2013b ) and Fritsch and Schindele ( 2011 ) have confirmed that Falck’s ( 2007 ) findings not only hold for the relationship between entrepreneurship and GDP but also for the relationship between entrepreneurship and employment.

3.3.7 Firm size

Baptista and Preto ( 2010 ) found that new firms of a larger than average initial size have a strong impact on employment and that this impact follows a pronounced wave-shaped time-lag structure (see Sect.  3.1 ). New firm formations which are smaller than average, on the other hand, only have a small impact. Acs and Mueller ( 2008 ) confirmed this finding and show that small new firms have a positive but declining direct impact on employment. The impact of medium and large new firms, however, is much higher and increases till it peaks in year five. Very large new firms (> 499 employees), however, decrease employment in the short- and medium-term, probably due to restructuring processes of incumbents. This empirical evidence suggests that up to a threshold, large new firms have a larger impact on employment.

3.3.8 Degree of internationalization

A less studied but yet empirically significant determinant is a firm’s degree of internationalization. Baptista and Preto ( 2010 ) analyzed 30 Portuguese regions and found that new firms which were, at least, partially owned by foreign investors had a much higher and more pronounced medium- and long-term impact on employment. A second measure of the positive impact of internationally active new firms is the export-orientation of new firms. Hessels and van Stel ( 2011 ) compared the impact of total-entrepreneurial activity and export-driven entrepreneurial activity on GDP per capita in 34 developed and developing countries. They found evidence that new firms for which the share of customers living abroad is above 26% have a more positive impact on GDP—but only in developed countries. González-Pernía and Peña-Legazkue ( 2015 ) confirmed their finding on a regional level by comparing OEA and export-oriented OEA in 17 Spanish regions. Besides a generally higher impact of export-oriented new firms, González-Pernía and Peña-Legazkue ( 2015 ) found that the impact increases with higher shares of foreign customers up to a threshold level. An earlier study by Fryges and Wagner ( 2008 ), who found a positive relationship between firm-level productivity and export-sales ratio, supports the evidence for a more positive impact of internationally active new firms.

3.3.9 Motivation

The literature review conducted for this paper provided eleven studies which empirically tested the macroeconomic importance of the entrepreneur’s motivations. All of these studies applied GEM-based data and definitions for opportunity-driven entrepreneurial activity (OEA) and necessity-driven entrepreneurial activity (NEA). Although four of these studies could not find a significant economic impact of OEA or NEA (Albulescu and Draghici 2016 ; Ferreira et al. 2017 ; Valliere and Peterson 2009 ; Wong et al. 2005 ), the other seven studies found evidence that OEA significantly increases national innovativeness (Acs and Varga 2005 ; Draghici and Albulescu 2014 ), competitiveness (Mrozewski and Kratzer 2017 ) and productivity (Du and O’Connor 2017 ; González-Pernía and Peña-Legazkue 2015 ; Ivanovic-Ðukic et al. 2018 ; Urbano and Aparicio 2016 ). Moreover, six of these seven studies confirmed that the impact of OEA is higher compared to NEA and TEA. Mrozewski and Kratzer ( 2017 ) even found NEA to decrease the national competitiveness.

3.3.10 Growth-ambitions

There are some entrepreneurs who not only seek to exploit a business-opportunity but also have high growth - ambitions for their new firms. All five empirical studies selected for this paper take GEM data on high-growth expectation entrepreneurship (HEA) as a measure of the entrepreneur’s growth - ambitions and found that it has a significantly positive impact on GDP-related measures of welfare. Furthermore, the impact of HEA seems to be more positive compared to TEA, to NEA and even to OEA (Ivanović-Đukić et al. 2018 ; Stam et al. 2011 ; Valliere and Peterson 2009 ; Wong et al. 2005 ). Generally, this macroeconomic impact of HEA seems to increase with the level of growth-aspiration (van Oort and Bosma 2013 ). The positive impact of HEA on economic welfare could be confirmed on the regional- and national-level as well as for developed countries. For less-developed countries, however, the empirical evidence is contradicting. On the one hand, Valliere and Peterson ( 2009 ) only found a significant impact of HEA on GDP for 25 developed countries, but not for the 18 emerging countries. On the other hand, Stam et al. ( 2011 ) found the impact of HEA on GDP in eight analysed lower-income to upper-middle-income economies (World Bank 2002 classification) even higher compared to the impact in the 22 analysed high-income economies.

3.3.11 Qualification

While many microeconomic studies have highlighted that an entrepreneur’s qualifications in terms of education (e.g. Kangasharju and Pekkala 2002 ), skills and experience (e.g. Brüderl et al. 1992 ; Baum et al. 2001 ; Unger et al. 2011 ) play a significant part in the success of new firms, only one of the studies empirically investigated the macroeconomic impact of education. This is an analysis of 3702 German firms conducted by Engel and Metzger ( 2006 ). It suggests that new firms founded by people with an academic degree may have a more positive direct employment effect, than firms founded by people without an academic degree. This finding is, however, based on an old dataset (1990–1993) and a simple descriptive comparison and the authors did not apply control variables such as the regional density of more educated people.

3.3.12 Gender and age

Only one study could be found which empirically analysed the economic impact of the entrepreneur’s gender and age . This study was conducted by Verheul and van Stel ( 2010 ) and was based on a dataset of 36 developed and developing countries. Their results show that there is a positive relationship between young opportunity-driven entrepreneurs between the ages of 18 and 24 and national GDP growth in developed countries, while in developing countries there is only a significant positive relationship between entrepreneurs aged between 45 and 64 and GDP growth (Verheul and van Stel 2010 ). Contrary to the microeconomic literature (e.g. Cliff 1998 ; Kalleberg and Leicht 1991 ; Rosa et al. 1996 ), Verheul and van Stel ( 2010 ) could not find any significant gender differences on the macroscale.

4 Roadmap for further research

The major scientific value and contribution of this paper lies in the groundwork for future research. Despite the extant of the reviewed existing research, many questions still remain unanswered. The following two sections therefore highlight the shortcomings of current research and make suggestions on how to address them. Section  4.1 discusses how remaining gaps in empirical research into the impact of entrepreneurship can be addressed and Sect.  4.2 presents fruitful research avenues on the determinants of the impact of entrepreneurship.

4.1 Implications for future research on the impact of entrepreneurship

4.1.1 more variety in the measures of entrepreneurship.

A high variety of measures of entrepreneurship is required to test the robustness of results but international comparative studies, in particular, are mainly based on just two entrepreneurship datasets: Comparative Entrepreneurship Data for International Analysis (COMPENDIA) based on OECD statistics and data from the GEM research project. The use of a high variety of entrepreneurship definitions and measures of entrepreneurship across studies makes it difficult to compare the results of these studies. While some studies simply estimate entrepreneurship based on self-employment rates or business-ownership rates, others measure entrepreneurship by counting new firm formations and firm exits or use holistic measures based on, e.g., Schumpeter’s understanding of entrepreneurship.

In order to test the robustness of the results and, at the same time, to allow for comparability between different studies, researchers should employ not one but multiple common measures of entrepreneurship in future studies. To make this possible, policy makers need to encourage the creation of internationally harmonized entrepreneurship databases. Furthermore, due to the limited availability of entrepreneurship data, only a few empirical studies have made a distinction between different types of entrepreneurship. That is why, as recommended by many researchers before (e.g. Baptista and Preto 2011 ; Fritsch and Schroeter 2011 ; Urbano et al. 2019a ), this study calls for more diversity in the application of measures of entrepreneurship.

4.1.2 Implementation of measures of social and environmental welfare

Section  3.1 revealed that 95.1% of the examined empirical studies only analysed the impact of entrepreneurship on economic welfare. Politicians who have no information on the impact of entrepreneurship on social and environmental welfare and thus solely rely on this economic information, however, may implement unsustainable development strategies (Tietenberg and Lewis 2012 ). Indeed, the few empirical studies (n = 5) which go beyond a traditional economic analysis indicate that entrepreneurship also has a significant contribution to measures of social and environmental welfare such as HDI, CO 2 emissions or poverty, which must not be neglected by politicians and researchers alike. To fill the immense gap in research on the impact of entrepreneurship on social and environmental welfare, two simultaneous approaches are proposed. First, as mentioned before, future research should generally include a variety of dependent welfare variables—social and environmental as well as economic ones. Second, future research should adopt research designs that have already proved effective in the macroeconomic impact analysis to answer novel research questions that address the impact of entrepreneurship on social and environmental welfare. The required methods for such analyses have been tested many times and, at least at national level, data availability poses no problem. Most countries have not only been collecting specific social and environmental welfare data for many years, but also established more holistic measures of welfare such as the Index of Sustainable Economic Welfare. Accordingly, it is up to the research community to break with traditions and expand the field of research by analysing social and environmental welfare rather than just economic welfare.

4.1.3 More research on developing countries

Section  3.3.4 illustrated that the local level of development is a relevant determinant of the impact of entrepreneurship. Nevertheless, most of the research reviewed for this paper focused solely on developed countries. This can partly be explained by the fact that most of the authors of these studies are based in Europe and the US, as well as by the lack of adequate long-term data for developing countries. However, this has begun to change. In the past 5 years, the number of empirical studies on developing countries has more than doubled to n = 30. Nevertheless, regional-level studies as well as long-term studies for developing countries remain scarce. Because of the growing importance of developing and particularly BRICS countries, it is important to increase the knowledge on how the impact of entrepreneurship manifests in these countries.

4.1.4 More studies on the lag-structure of the impact of entrepreneurship

Section  3.1 illustrates that although the important indirect impact of entrepreneurship requires 5 or more years to unfold, most empirical research focuses on the direct short-term impact. Neglecting the long-term effects of entrepreneurship therefore results in an incomplete picture. Furthermore, the analysis of longitudinal data is required to conduct relevant causality tests. So far, the bottleneck for national-level long-term studies has been the lack of longitudinal data. But, due to more than 20 years of worldwide data collection for the GEM, there is now at least one sufficiently large entrepreneurship database. In line with other authors who have recognised this issue (e.g. Baptista et al. 2008 ; Carree and Thurik 2008 ; Fritsch 2013 ), this paper recommends that all future research should analyse not only the short-term but also the medium- and long-term impact of entrepreneurship.

4.2 Implications for future research on determinants

Table  3 summarizes key statistics for the determinants in the research reviewed for this paper. Comparing the last two rows, it seems that the studies analysing the determinants of the impact of entrepreneurship are a representative share of all reviewed studies. For this reason, the previously presented suggestions for future research also apply to literature on the determinants. On closer examination, however, Table  3 reveals further and more precise research gaps. These include, inter alia, the need to study particularly the environmental and firm level determinants in developing countries, and the analysis of individual level determinants in combination with the lag-structure of the impact of entrepreneurship. The requirement for more long-term studies is further highlighted here. This finding further specifies the previous call for more long-term studies. The following subsections present further research and research implications.

4.2.1 More variety in measures of entrepreneurship

Table  3 shows that research on environmental and firm level determinants are mainly based on new firm formations as a measure of entrepreneurship, and research on individual level determinants almost solely measures entrepreneurship using GEM data.

The only exceptions are studies on the determinants local level of development —which are comparing the entrepreneurial impact across countries and thus are also mostly based on GEM data—and on innovativeness . None of the studies on the determinants apply self-employment (for the sake of clarity not presented in Table  3 ) to estimate entrepreneurship. This illustrates that the research on all individual determinants, except for innovativeness , considerably lacks variety when it comes to the applied measures of entrepreneurship.

4.2.2 More variety in measures of welfare

In addition to the fact that there are no studies examining the determinants of the impact of entrepreneurship on social or environmental welfare, there is also a lack of variety in the studies of measures of economic welfare. Studies on all individual level determinants and particularly on the determinant local level of development almost exclusively analyse the impact of entrepreneurship on GDP-related measures of welfare. Studies on the determinants industry affiliation , population density , firm survival and firm size mainly analyse employment effects of entrepreneurship. Other common measures of economic welfare, such as innovativeness or competitiveness, are rarely studied and need further investigation.

4.2.3 Further research on determinants

Table  3 illustrates that the existing research is imbalanced and that it pays varying degrees of attention to individual determinants. Determinants such as innovativeness , motivations and most environmental level determinants have so far received a great deal of attention, while others have only been analysed in very few studies. However, some of these poorly researched factors promise to be relevant determinants. More specific, the few existing empirical results analysing firm survival , degree of internationalisation and growth - ambitions suggest that these determinants have a comparatively high effect on the relationship between entrepreneurship and economic welfare. Furthermore, these determinants as well as the largely unexplored determinant qualifications are of considerable practical and political relevance. More empirical research on these determinants and their moderating role is required to improve incentives and support programs for entrepreneurs.

4.2.4 New research focus on determinants not yet empirically investigated

Table  4 provides a short overview of determinants which are likely to shape the entrepreneurial macroeconomic impact, but which have not yet been empirically investigated. They are a selection of indicators which are believed to determine the impact of entrepreneurship on economic welfare or which are empirically related to the success and survival of new firms and thus are also likely to be of macroeconomic importance. The overview is based on a non-systematic scan of the microeconomic literature and makes no claim to completeness. Due to their particularly high microeconomic relevance highlighted by the authors listed in Table  4 , this paper specifically proposes additional research on how firm performance, organisational structure and strategies, networking activities and motivations (beyond necessity and opportunity entrepreneurship) determine the impact of entrepreneurship.

4.2.5 Methodological recommendations

Many of the determinants discussed here are highly interdependent, which makes it very difficult to extract and examine their separate effects. Individual level characteristics and environmental conditions are especially likely to affect the impact of entrepreneurship mainly indirectly through firm performance. The complexity is increased further as determinants may be indicators for other macroeconomically relevant effects. For instance, the numbers of highly innovative new firms and of highly qualified entrepreneurs may be positively correlated with the excellence of the regional educational infrastructure. This in turn could mean that the excellence of educational infrastructure is the true reason for economic growth and innovative new firms and highly qualified entrepreneurs have little or no economic impact but are merely indicators for the educational infrastructure. However, little is currently known about such interdependencies and research is required which particularly studies the path dependencies behind the impact of entrepreneurship. This is why future empirical research should examine determinants which are supposed to be interdependent as well as external effects which may be related to the determinants of interest.

5 Limitations and conclusion

This paper has shed light on the impact of entrepreneurship on economic welfare and the determinants of this impact, but it is not without limitations. First, this paper seeks to give a comprehensive overview of the empirical research, but the search was limited by a variety of in- and exclusion criteria as well as by the terms used in the search string. Although the exclusive focus on peer-reviewed articles is common practice in systematic literature reviews, this may have led to the systematic exclusion of potentially relevant research outcomes, e.g. from dissertation, book chapters, conference contributions or working papers. Furthermore, it is possible that individual studies were not identified by the automated search for the search string in keywords, titles and abstracts. These limitations were necessary to reduce the search results to a manageable level and to ensure a certain quality of the results. The additional screening of key journals, meta-studies and reviews as well as the applied back- and forward snowballing approach, however, weaken the effects of these limitations. Second, this paper only deals with empirical studies. The inclusion of qualitative studies might have revealed further studies dealing with the impact of entrepreneurship on environmental and social welfare. Additionally, the exclusion of qualitative studies limits the analytical depth within the discussion of the determinants. Third, the paper focused on research on a few selected measures of entrepreneurship. In doing so, intrapreneurship, entrepreneurship culture or diverse composed entrepreneurial activity measures of entrepreneurship were excluded. Fourth, it needs to be stated that large parts of the data selection and synthesis were only conducted by the author. Although the chosen procedure and the frequent consultation with the research panel reduced the likelihood of biases, the chance remains that the review is burdened with subjectivity and selection biases. Finally, the scope of this paper was to provide a first descriptive summary of the determinants analysed in the empirical literature and to derive research recommendation. Due to this clear focus this paper does not comprise extensive bibliometric- or meta-analyses that describe in detail the general literature on the impact of entrepreneurship.

The systematic review presented in this paper was conducted for three main reasons. First, to summarize the current state of empirical research on the impact of entrepreneurship on economic, social and environmental welfare. Second, to identify the determinants of this impact and third, to develop a roadmap for future research. Due to the application of a broad entrepreneurship definition and due to the incorporation of economic, social and environmental welfare, this paper presents the most comprehensive overview, summary and synthesis of empirical research on this topic to date. The results confirm the findings and theories of previous literature reviews on the impact of entrepreneurship, provide an update and extension to the current knowledge and finally, represent a first attempt to structure the determinants of the impact of entrepreneurship. The new determinants-driven perspective on the research field reveals several shortcomings that would otherwise have gone unnoticed. The developed roadmap for future research—combined with a higher variety of applied measures of entrepreneurship and with an increased awareness of causality and interdependency issues—will allow future researchers to unravel the complex relationship between entrepreneurship and welfare and therewith to provide politicians the comprehensive information they need to promote the right types of entrepreneurship in the right situations.

For purposes of this study, the three welfare dimensions refer to the widely used definition of the three pillars of sustainable development (economic growth, social equality protection, environmental protection) of the Brundtland Report (World Development Commission on Environment and Development 1987 ). However, the reader should note that later sustainability models like the ‘prism model’ or the ‘concentric circles model’ illustrate that the three pillars of sustainable development (resp. the three welfare dimensions) are interlinked and not always clearly separable from one another.

Although the author is fully aware of their different meanings, for simplicity, the more general term ‘economic welfare’ is used throughout this paper as synonymous with the terms ‘economic growth’ and ‘economic development’.

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Neumann, T. The impact of entrepreneurship on economic, social and environmental welfare and its determinants: a systematic review. Manag Rev Q 71 , 553–584 (2021). https://doi.org/10.1007/s11301-020-00193-7

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Technology and the Innovation Economy

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Darrell m. west darrell m. west senior fellow - center for technology innovation , douglas dillon chair in governmental studies.

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Executive Summary

Innovation and entrepreneurship are crucial for long-term economic development. Over the years, America’s well-being has been furthered by science and technology. Fears set off by the Soviet Union’s 1957 launch of its Sputnik satellite initiated a wave of U.S. investment in science, engineering, aerospace, and technology. Both public and private sector investment created jobs, built industries, fueled innovation, and propelled the U.S. to leadership in a number of different fields.

In this paper, I focus on ways technology enables innovation and creates economic prosperity. I review the range of new advances in education, health care, and communications, and make policy recommendations designed to encourage an innovation economy. By adopting policies such as a permanent research and development tax credit, more effective university knowledge commercialization, improving STEM worker training, reasonable immigration reform, and regional economic clusters, we can build an innovation economy and sustain our long-term prosperity.

The Link to Economic Prosperity

Researchers have found a link between technology innovation and national economic prosperity. For example, a study of 120 nations between 1980 and 2006 undertaken by Christine Qiang estimated that each 10 percentage point increase in broadband penetration adds 1.3 percent to a high income country’s gross domestic product and 1.21 percent for low to middle-income nations. [i]

In addition, Taylor Reynolds has analyzed the role of communication infrastructure investment in economic recoveries among OECD countries and found that nearly all view technology development as crucial to their economic stimulus packages. [ii] He demonstrates that there is a strong connection between telecommunication investment and economic growth, especially following recessions. These kinds of investments help countries create jobs and lay the groundwork for long-term economic development.

As a result, many nations around the world are investing in digital infrastructure as a way to jump-start economies weakened by the recent financial collapse. The decline in stock market valuations, rise in unemployment, and reduction in overall economic growth has highlighted the need to target financial resources and develop national priorities. In conditions of economic scarcity, countries no longer have the luxury of being passive and reactive. Instead, they must be proactive and forward-looking, and think clearly about how to create the basis for sustainable economic recoveries.

Not surprisingly, given its long-term potential, a number of countries have identified information technology as a crucial infrastructure need for national development. Broadband is viewed in many places as a way to stimulate economic development, social connections, and civic engagement. National leaders understand that cross-cutting technology speeds innovation in areas such as health care, education, communications, and social networking. When combined with organizational changes, digital technology can generate powerful new efficiencies and economies of scale. [iii]

People Understand Importance of Innovation, But Doubt U.S. Future

Despite the importance of the connection between technology innovation and economic prosperity, public opinion surveys reveal interesting results in people’s views about innovation. A 2009 Newsweek -Intel Global Innovation Survey interviewed 4,800 adults in the United States, China, United Kingdom, and Germany.  Researchers found that “two-thirds of respondents believe innovation will be more important than ever to the U.S. economy over the next 30 years.” [iv]   People understand the basic point that innovation has been key to past prosperity and is vital moving forward.

The survey also found interesting differences between Americans and the Chinese in what they think is important to future advances. According to the survey, “Americans are focused on improving math and science education, while Chinese are more concerned about developing creative problem-solving and business skills.” [v] Apparently, people from the respective nations have different fears about their current innovation training and what is necessary for future innovation.

However, there is a remarkable divergence between Americans and Chinese in assessments of the contemporary situation. Americans are remarkably pessimistic about their own future.  When asked how the U.S. was doing in 2009, only 41 percent of Americans thought our country was ahead of China on innovation compared to 81 percent of Chinese who felt the U.S. was ahead. [vi] Americans worried that their country was falling behind on innovation while other countries were moving forward.

There are objective reasons behind this American pessimism. There are too few Americans studying the traditional STEM fields of science, technology, engineering, and math. Due to our immigration policy, it is difficult for foreign students who are educated in the United States to stay here, get jobs, and contribute to American innovation the way many immigrants have done in the U.S. previously. [vii]   With our current debt and budget deficit levels, Americans worry about our long-term ability to invest in education and research in the way we did in the past and produce positive results.

An analysis of patents granted shows that our country’s long-term dominance has come to an end.  In 1999, American scientists were granted 90,000 patents, compared to 70,000 for those from all other countries. [viii]   By 2009, though, non-U.S. innovators earned more patents (around 96,000) compared to Americans (93,000). This represented the first time in recent years where non-Americans had garnered more patents. [ix]

The United States spends only 2.8 percent of its federal budget on national research and development as a percentage of GDP. This is less than the 4.3 percent spent by the government in Sweden, 3.1 percent by Japan, and 3.0 percent by South Korea, but higher than that of Germany (2.5 percent), France (2.2 percent), Canada (1.9 percent), or England (1.9 percent). Europe as a whole devotes 1.9 percent to research and development, while industrialized nations spend around 2.3 percent. [x]

If one adds together all the science and technology workers in the United States as a percentage of the workplace, 33 percent of American employees have science or technology positions. This is slightly less than the 34 percent figure for the Netherlands and Germany, but higher than the 28 percent in France and Canada,. [xi]

The productivity in this area has fueled considerable demand for those with science and engineering expertise, and it has been difficult for the United States to produce sufficient knowledge workers. [xii]   Thirty-eight percent of Korean students now earn degrees in science and engineering, compared to 33 percent for Germany, 28 percent for France, 27 percent for England, and 26 percent for Japan. The United States has fallen behind in this area.  Despite great demand for this kind of training, only 16 percent of American graduates have backgrounds in science and engineering. [xiii]

In America, the private sector surpassed the federal government in 1980 in terms of the amount of money spent on research and development. By 2003, commercial companies provided 68 percent of the $283 billion spent on research and development, compared to 27 percent from the federal government. Of this total, $113 billion came from the federal government, while $170 came from the private sector. According to information from the National Science Board, the percentage of research and development spending coming from the federal government has dropped from around 63 percent in the early 1960s to 27 percent today, while that of the private sector increased from 30 to 68 percent. [xiv]

The Need for a Clear Focus on Innovation

In moving forward, it is clear that information technology enables innovation in a variety of policy areas.  According to Philip Bond, the president of TechAmerica, “each tech job supports three jobs in other sectors of the economy.” And in information technology, he says, there are five jobs for each IT position. [xv]

Faster broadband and wireless speeds also enable people to take advantage of new digital tools such as GIS mapping, telemedicine, virtual reality, online games, supercomputing, video on demand, and video conferencing.  New developments in health information technology and mobile health, such as emailing X-rays and other medical tests, require high-speed broadband. And distance learning, civic engagement, and smart energy grids require sufficient bandwidth. [xvi]

High-speed broadband allows physicians to share digital images with colleagues in other geographic areas.  Schools are able to extend distance learning to under-served populations. Smart electric grids produce greater efficiency in monitoring energy consumption and contribute to more environment-friendly policies.  Video conferencing facilities save government and businesses large amounts of money on their travel budgets. New digital platforms across a variety of policy domains spur utilization and innovation, and bring additional people, businesses, and services into the digital revolution.

In the education area, better technology infrastructure enables personalized learning and real-time assessment. Imagine schools where students master vital skills and critical thinking in a personalized and collaborative manner, teachers assess pupils in real-time, and social media and digital libraries connect learners to a wide range of informational resources. Teachers take on the role of coaches, students learn at their own pace, technology tracks student progress, and schools are judged based on the outcomes they produce. Rather than be limited to six hours a day for half the year, this kind of education moves toward 24/7 engagement and learning fulltime. [xvii]  

These represent just a few of the examples where innovation is taking place. Technology fosters innovation, creates jobs, and boost long-term economic prosperity. By improving communication and creating opportunities for data-sharing and collaboration, information technology represents an infrastructure issue as important as bridges, highways, dams, and buildings.

Getting Serious about Innovation Policy

To stimulate innovation, we need a number of policy actions. Right now, the United States does not have a coherent or comprehensive innovation strategy. Unlike other nations, who think systematically about these matters, we make policy in a piecemeal fashion and focus on short versus long-term objectives. This limits the efficiency and effectiveness of our national efforts. There are a number of areas that we need to address.

Research and Development Tax Credits : An example of our national short-sightedness is the research and development tax credit.  Members of Congress have extended this many times in recent years, but they generally do this on an annual basis.  Rather than extend this credit over a long period of time, they renew it episodically and never on a predictable schedule.

This makes it difficult for companies to plan investments and pursue consistent strategies over time. Due to political uncertainties and institutional politics, we end up creating inefficiencies linked to the vagaries of federal policymaking. [xviii] While companies in other countries invest and deduct on a more predictable schedule, we shoot ourselves in the foot through a short-sighted perspective.  Bond notes that “23 countries now offer a more generous and stable credit” than the United States. [xix]

Commercializing University Knowledge : Universities represent a crucial linchpin in efforts to build an innovation economy.  They are extraordinary knowledge generators, but must do a better job of transferring technology and commercializing knowledge. University licensing offices must speed up their review process in order to encourage the formation of businesses. Universities should think more seriously about innovation metrics so they allocate resources efficiently and create the proper incentives.

Right now, many places count the number of patents and licensing agreements without much attention to the businesses created, products that are marketed, or revenue that is generated. They should make sure their resources and incentives are aligned with metrics that encourage technology transfer and commercialization. [xx]

STEM Workforce Training and Development : The United States is facing a crisis in STEM training and workforce development. There are many dimensions of this challenge, but one of the most important concerns is the low number of college students graduating with degrees in science, technology, engineering, and math. Few American students are developing proficiency in these subjects, which is hindering the country’s economic future. Past American prosperity has been propelled by advances in the STEM fields.   Skills in these areas helped the country win the space race and the Cold War and we need them now as we transition to a technology driven economy.

To deal with this problem, President Barack Obama’s Council of Advisors on Science and Technology (PCAST) has produced an official report that calls for the creation of a Master Teachers Corps. Among other recommendations, the report emphasizes two actions: 1) hiring 100,000 new STEM teachers and 2) paying higher salaries to the top 5 percent of STEM teachers. [xxi]   However, in an era of budget cutbacks and attacks on teacher unions, it has been difficult to build support for raising teacher salaries in general and adopting differential pay in particular.

In his 2011 State of the Union, the President restated his commitment to putting education at the forefront of the national agenda, emphasizing the need for quality teachers, investment in STEM education programs, and a “bold restructuring” of federal education funding. He called for identifying effective teachers and creating reward systems to retain top-performing individuals.

It is vital to address these issues because basic facts about STEM teaching and competency are not well known.  Failing schools not only harm students, they weaken the overall economy. With the U.S. facing a crisis of massive proportions in terms of its ability to innovate and create jobs, it is imperative that we transform STEM teaching to prepare students for the future economy. Real emphasis should be placed on teacher investment because research has shown that teachers are the primary factor in ensuring student growth and achievement.

An Einstein Strategy for Immigration Reform : We need reasonable immigration reform. One of our most important challenges is a new narrative defining immigration as a brain gain that improves economic competitiveness and national innovation. A focus on brains and competitiveness would help America overcome past deficiencies in immigration policy and enable our country to move forward into the 21 st century. It is a way to become more strategic about promoting our long-term economy and achieving important national objectives. [xxii]

We need to think about immigration policy along the lines of an “Einstein Principle.” In this perspective, national leaders would elevate brains, talent, and special skills to a higher plane in order to attract more individuals with the potential to enhance American innovation and competitiveness. The goal is to boost the national economy, and bring individuals to America with the potential to make significant contributions.  This would increase the odds for prosperity down the road. It has been estimated that “over 50,000 workers with advanced degrees leave the country for better opportunities elsewhere.” [xxiii]

O-1 Genius Visas : In order to boost American innovation, current policy contains a provision for a visa “brains” program. The so-called “genius” visa known as O-1 allows the government to authorize visas for those having “extraordinary abilities in the arts, science, education, business, and sports.” In 2008, around 9,000 genius visas were granted, up from 6,500 in 2004.  The idea behind this program is to focus on talented people and encourage them to come to the United States. It is consistent with what national leaders have done in past eras, where we encouraged those with special talents to migrate to our nation.

However, this program has been small and entry passes have gone to individuals such as professional basketball player Dirk Nowitzki of Germany and various members of the Merce Cunningham and Bill T. Jones/Arnie Zane dance companies. [xxiv] While these people clearly have special talents, it is important to extend this program in new ways and target people who create jobs and further American innovation.  This would help the United States compete more effectively.

EB-5 Job Creation Visas : There is a little-known EB-5 visa program that offers temporary visas to foreigners who invest at least half a million dollars in American locales officially designated as “distressed areas.” If their financial investment leads to the creation of 10 or more jobs, the temporary visa automatically becomes a permanent green card.  Without much media attention, there were 945 immigrants in 2008 who provided over $400 million through this program. [xxv] On a per capita basis, these benefits make the program one of the most successful economic development initiatives in the federal government.

This is a great way to tie U.S. immigration policy to job creation. If a goal of national policy is to encourage investment and job creation, targeted visas of this sort are very effective.  Such programs explicitly link new immigration with concrete economic investment. They also generate needed foreign capital ($500,000) for poor geographic areas. There is public accountability for this policy program because entry visas are granted on a temporary basis and become permanent only AFTER at least 10 jobs have been created.  This kind of visa program is the ultimate in targeting and quality control. Unless the money is invested and leads to new jobs, the newcomer is not allowed to stay in the United States.

H-1B Worker Visas : Right now, only 15 percent of annual visas are set aside for employment purposes.  Of these, some go to seasonal agricultural workers, while a small number of H-1B visas (65,000) are reserved for “specialty occupations” such as scientists, engineers, and technological experts. Individuals who are admitted with this work permit can stay for up to six years, and are able to apply for a green card if their employer is willing to sponsor their application.

The number reserved for scientists and engineers is drastically below the figure allowed between 1999 and 2004. In that interval, the federal government set aside up to 195,000 visas each year for H-1B entry.  The idea was that scientific innovators were so important for long-term economic development that we needed to boost the number set aside for those specialty professions.

Today, most of the current allocation of 65,000 visas run out within a few months of the start of the government’s fiscal year in October.  Even in the recession-plagued period of 2009, visa applications exceeded the supply within the first three months of the fiscal year. American companies were responsible for 49 percent of the H-1B visa requests in 2009, up from 43 percent in 2008. The companies which were awarded the largest number of these visas included firms such as Wipro (1,964), Microsoft (1,318), Intel (723), IBM India (695), Patri Americas (609), Larsen & Toubro Infotech (602), Ernst & Young (481), Infosys technologies (440), UST Global (344), and Deloitte Consulting (328). [xxvi]

High-skill visas need to be expanded back to 195,000 because at its current level, that program represents only six and a half percent of the million work permits granted each year by the United States. That percentage is woefully inadequate in terms of the supply needed. Entry programs such as the H-1B, O-1, and L-1 visa programs grant temporary visas for a period of a few years to workers with special talents needed by American employers. They enable U.S. companies to attract top people to domestic industries, and represent a great way to encourage innovation and entrepreneurship.

Regional Economic Clusters : We need regional economic clusters that take advantage of innovation-rich geographic niches. There are several examples of successful and geographically-based clusters such as Silicon Valley, Boston’s Route 128, and the Research Triangle in North Carolina. In each of these areas, there is a combination of creative talent associated with terrific universities, access to venture capital, and state laws that promote innovation through tax policy and/or infrastructure development.

Research has demonstrated that these innovation clusters generate positive economic results. According to a Brookings report by Mark Muro and Bruce Katz, “it is now broadly affirmed that strong clusters foster innovation through dense knowledge flows and spillovers; strengthen entrepreneurship by boosting new enterprise formation and start-up survival, enhance productivity, income-levels, and employment growth in industries, and positively influence regional economic performance.” [xxvii]

The question is how to promote such clusters in other geographic areas. There clearly are other places with the underlying conditions that foster technology innovation. But Muro and Katz caution that political leaders can’t force clusters that don’t already exist and that they should let the private sector lead in encouraging cluster formation. It is important to leverage existing resources and take advantage of workforce development programs, banking rules, educational institutions, and tax policies. [xxviii]

[i] Christine Zhen-Wei Qiang, “Telecommunications and Economic Growth,” Washington, D.C.:  World Bank, unpublished paper.

[ii] Taylor Reynolds, “The Role of Communication Infrastructure Investment in Economic Recovery,” Working Party on Communication Infrastructures and Services Policy, OECD, March, 2009.

[iii] Erik Brynjolfsson and Adam Saunders, Wired for Innovation, Cambridge, Massachusetts:  MIT Press, 2009.

[iv] Daniel McGinn, “The Decline of Western Innovation:  Why America is Falling Behind and How to Fix It,” The Daily Beast, November 15, 2009.

[v] Daniel McGinn, “The Decline of Western Innovation:  Why America is Falling Behind and How to Fix It,” The Daily Beast, November 15, 2009.

[vi] Daniel McGinn, “The Decline of Western Innovation:  Why America is Falling Behind and How to Fix It,” The Daily Beast, November 15, 2009.

[vii] Darrell West, Brain Gain:  Rethinking U.S. Immigration Policy, Washington, D.C.:  Brookings Institution Press, 2010.

[viii] Darrell M. West, Biotechnology Policy Across National Boundaries, New York:  Palgrave/Macmillan, 2007.

[ix] Michael Arndt, “Ben Franklin, Where Are You?” Business Week, January 4, 2010, p. 29.

[x] Organisation for Economic Co-Operation and Development, Science and Technology Statistical Compendium, 2004.

[xi] Organisation for Economic Co-Operation and Development, Science and Technology Statistical Compendium, 2004.

[xii] Darrell West, Brain Gain:  Rethinking U.S. Immigration Policy, Washington, D.C.:  Brookings Institution Press, 2010.

[xiii] Organisation for Economic Co-Operation and Development, Science and Technology Statistical Compendium, 2004.

[xiv] National Science Board, “Science and Engineering Indictors 2004,” Washington, D.C.:  National Science Foundation, 2004, p. 0-4.

[xv] Philip Bond, “Tech Provides Map for Nation’s Future,” Politico, September 18, 2011.

[xvi] Darrell West, “An International Look at High-Speed Broadband,” Washington, D.C.:  Brookings Institution, February, 2010.

[xvii] Darrell West, “Using Technology to Personalize Learning and Assess Students in Real-Time,” Washington, D.C.:  Brookings Institution, October 6, 2011.

[xviii] Martin Baily, Bruce Katz, and Darrell West, “Building a Long-Term Strategy for Growth through Innovation,” Washington, D.C.:  Brookings Institution, May, 2011.

[xix] Philip Bond, “Tech Provides Map for Nation’s Future,” Politico, September 18, 2011.

[xx] Martin Baily, Bruce Katz, and Darrell West, “Building a Long-Term Strategy for Growth through Innovation,” Washington, D.C.:  Brookings Institution, May, 2011.

[xxi] President’s Council of Advisors on Science and Technology, “Prepare and Inspire:  K-12 Education in Science, Technology, Engineering, and Math for America’s Future,” September, 2010.

[xxii] Richard Herman and Robert Smith, Immigrant, Inc.:  Why Immigrant Entrepreneurs Are Driving the New Economy and How They Will Save the American Worker, Hoboken, New Jersey:  John Wiley & Sons, 2010.

[xxiii] Center for Public Policy Innovation, “Restoring U.S. Competitiveness:  Navigating a Path Forward Through Innovation and Entrepreneurship,” Washington, D.C., September 7, 2011.

[xxiv] Moira Herbst, “Geniuses at the Gate,” Business Week, June 8, 2009, p. 14.

[xxv] Lisa Lerer, “Invest $500,000, Score a U.S. Visa,” CNNMoney.com.

[xxvi] Moira Herbst, “Still Wanted:  Foreign Talent—And Visas,” Business Week, December 21, 2009, p. 76.

[xxvii] Mark Muro and Bruce Katz, “The New ‘Cluster Moment’:  How Regional Innovation Clusters Can Foster the Next Economy,” Washington, D.C.:  Brookings Institution, September 21, 2010.

[xxviii] Mark Muro and Bruce Katz, “The New ‘Cluster Moment’:  How Regional Innovation Clusters Can Foster the Next Economy,” Washington, D.C.:  Brookings Institution, September 21, 2010.

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Guest Essay

Many Americans Believe the Economy Is Rigged

A color photo of a cracked sidewalk with a large puddle at its center. There is a reflection of the top of the U.S. Capitol building in the puddle.

By Katherine J. Cramer and Jonathan D. Cohen

Ms. Cramer is a co-chair of the Commission on Reimagining Our Economy at the American Academy of Arts & Sciences. Mr. Cohen is a senior program officer at the American Academy of Arts & Sciences.

When asked what drives the economy, many Americans have a simple, single answer that comes to mind immediately: “greed.” They believe the rich and powerful have designed the economy to benefit themselves and have left others with too little or with nothing at all.

We know Americans feel this way because we asked them. Over the past two years, as part of a project with the American Academy of Arts and Sciences, we and a team of people conducted over 30 small-group conversations with Americans from almost every corner of the country. While national indicators may suggest that the economy is strong, the Americans we listened to are mostly not thriving. They do not see the economy as nourishing or supporting them. Instead, they tend to see it as an obstacle, a set of external forces out of their control that nonetheless seems to hold sway over their lives.

Take the perceived prevalence of greed. This is hardly a new feeling, but it has been exacerbated recently by inflation and higher housing costs. Americans experience these phenomena not as abstract concepts or political talking points but rather as grocery stores and landlords demanding more money.

Income inequality has been in decline over the last few years. But try explaining that to someone struggling to pay the rent. “I just feel like the underdog can’t get ahead, and it’s all about greed and profit,” one Kentucky participant noted. It is not necessarily the actual distribution of wealth that troubles people. It is the feeling that the economy is rigged against them.

There is a clear disconnect between the macroeconomic story and the micro-American experience. While a tight job market has produced historic gains for lower-income workers, many of the low-income workers we spoke with are unable to accumulate enough money to build a safety net for themselves. “I like the feeling of not living on the edge of disaster,” a special-education teacher in rural Tennessee said. “[I am] at my fullest potential economically” right now, but “I’m still one doctor’s visit away from not being there, and pretty much most people I know are.”

If there is a singular explanation for dissatisfaction with the economy, it is a lack of financial certainty. While direct government assistance early in the pandemic certainly helped many in 2020 and 2021, millions of households still struggled to get food, and many millions fell behind on rent. These feelings of instability do not dissipate quickly, especially when rising prices make trips to the store adventures in budgetary arithmetic and the threat of an accident or a surprise medical bill looms around every corner. “Uncertainty really affects your well-being. It affects what you do. It affects how you behave,” said a unionized airport worker in Virginia who tutors in the evenings.

An absence of economic resilience prevents people from spending time with family, from getting involved in their community and from finding ways to build a safety net. “The way the economy is going right now, you don’t know where it’s going to be tomorrow, next week,” a human resources employee in Indiana said. Well-being “is about being financially stable. It’s not about being rich, but it’s about being able to take care of your everyday needs without stressing.”

Stress is a rampant part of American life, much of it caused by financial insecurity. Some people aspire for the mansion on the hill. Many others are looking just to get their feet on solid ground.

One does not need to look hard beyond traditional metrics to see the prevalence of insecurity. In June an industry report found that auto loan delinquencies were higher than they were at the peak of the Great Recession. Credit card use has swelled, and delinquencies are at among their highest rates in a decade. After hitting a historic low in 2021 thanks to the expansion of the child tax credit, child poverty more than doubled in 2022 after the tax credit’s expansion expired. Also in 2022, rates of food insecurity reached their highest levels since 2015.

Such trends do not affect all Americans equally. Most disproportionately affect Black and Hispanic households, which perhaps helps explain Republicans’ gains in these communities, according to recent polls. Geography plays a major role, too. In some parts of the country — particularly rural areas — many people feel they have been left out of the progress and promise of the high-tech economy. Even if their finances remain in good health, they seem to fear for the future of their community, and they blame the economy.

The political system is supposed to make all this better. Instead, even as both major parties have vied to cast themselves as the standard-bearer of the working class, many Americans see politicians as unable or unwilling to do anything to help them. “In my democracy, I’d like to see us get rid of Republicans, Democrats,” one Kentucky participant told us. “Just stand up there, tell me what you can do. If you can do it, I don’t have to care what you are.” Many Americans seem to see Washington as awash in partisan squabbles over things that have little effect on their lives. Many believe that politicians are looking out for their political party, not the American people.

It should not be surprising, then, that so many are so pessimistic about a seemingly strong economy. A rising gross domestic product lifts lots of boats, but many Americans feel as if they are drowning.

What would make the people we talked to less stressed? The ability to accumulate savings. Low-wage workers have seen their incomes rise only for many of these gains to be wiped out by inflation. And the costs of housing, health care and child care can quickly absorb even a very robust rainy-day fund. Without a safety net that can propel people into security, the threat of these costs will continue to make many Americans feel unstable, uncertain and decidedly unhappy about the economy.

A helpful starting point would be to address benefit cliffs — income eligibility cutoffs built into certain benefits programs. As households earn more money, they can make themselves suddenly ineligible for benefits that would let them build up enough wealth to no longer need any government support. In Kansas, for example, a family of four remains eligible for Medicaid as long as it earns under $39,900. A single dollar in additional income results in the loss of health care coverage — and an alternative will certainly not cost only a buck.

Reforming these types of cliffs for health care, child care, housing and food assistance programs would allow the millions of households receiving state aid to achieve a sense of stability. Take this mother in Chicago who told us that her income is just above the eligibility cutoffs. The cliff “knocks me out of a lot of the opportunity to qualify for a lot of the programs that could assist in benefiting myself and my child,” she said.

The Americans we listened to want resiliency so they can feel that they are in control of their lives and that they have a say in the direction of their community and their nation. They want a system focused less on how the economy is doing and more on how Americans are doing. As one Houston man observed: “We’re so far down on the economic chain that we don’t have nothing. It seems like our voices don’t matter.” But they do matter. The rest of us just need to listen.

Katherine J. Cramer is a political science professor at the University of Wisconsin, Madison. Jonathan D. Cohen is the author of “For a Dollar and a Dream: State Lotteries in Modern America.”

The Times is committed to publishing a diversity of letters to the editor. We’d like to hear what you think about this or any of our articles. Here are some tips . And here’s our email: [email protected] .

Follow the New York Times Opinion section on Facebook , Instagram , TikTok , X and Threads .

Data, Privacy Laws and Firm Production: Evidence from the GDPR

By regulating how firms collect, store, and use data, privacy laws may change the role of data in production and alter firm demand for information technology inputs. We study how firms respond to privacy laws in the context of the EU’s General Data Protection Regulation (GDPR) by using seven years of data from a large global cloud-computing provider. Our difference-in-difference estimates indicate that, in response to the GDPR, EU firms decreased data storage by 26% and data processing by 15% relative to comparable US firms, becoming less “data-intensive.” To estimate the costs of the GDPR for firms, we propose and estimate a production function where data and computation serve as inputs to the production of “information." We find that data and computation are strong complements in production and that firm responses are consistent with the GDPR, representing a 20% increase in the cost of data on average. Variation in the firm-level effects of the GDPR and industry-level exposure to data, however, drives significant heterogeneity in our estimates of the impact of the GDPR on production costs.

We thank Guy Aridor, James Brand, Alessandro Bonatti, Peter Cihon, Jean Pierre Dubé, Joe Doyle, Ben Edelman, Liran Einav, Sara Ellison, Maryam Farboodi, Samuel Goldberg, Yizhou Jin, Garrett Johnson, Gaston Illanes, Markus Mobius, Devesh Raval, Dominik Rehse, Tobias Salz, Bryan Stuart, Taheya Tarannum, Joel Waldfogel, and Mike Whinston for helpful comments, and Abbie Natkin, Taegan Mullane, Doris Pan, Ryan Perry, Bea Rivera for excellent research assistance. We are also grateful to Han Choi for copyediting assistance. We gratefully acknowledge the support of the National Institute on Aging, Grant Number T32- AG000186 (Li) and the National Science Foundation Graduate Research Fellowship under Grant No 214106 (Li). The views expressed herein are those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Chicago, the Federal Reserve System, or the National Bureau of Economic Research.

Mert Demirer is a former paid postdoctoral researcher at Microsoft (a firm active in the cloud market, which this paper studies).

Diego Jiménez Hernández is a former paid postdoctoral researcher at Microsoft.

Dean Li is a former intern at Microsoft.

Sida Peng is a paid employee and minority equity holder at Microsoft.

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Circular economy: definition, importance and benefits

The circular economy: find out what it means, how it benefits you, the environment and our economy.

research paper about economy

The European Union produces more than 2.2 billion tonnes of waste every year . It is currently updating its legislation on waste management to promote a shift to a more sustainable model known as the circular economy.

But what exactly does the circular economy mean? And what would be the benefits?

What is the circular economy?

The circular economy is a model of production and consumption , which involves sharing, leasing, reusing, repairing, refurbishing and recycling existing materials and products as long as possible. In this way, the life cycle of products is extended.

In practice, it implies reducing waste to a minimum. When a product reaches the end of its life, its materials are kept within the economy wherever possible thanks to recycling. These can be productively used again and again, thereby creating further value .

This is a departure from the traditional, linear economic model, which is based on a take-make-consume-throw away pattern. This model relies on large quantities of cheap, easily accessible materials and energy.

Also part of this model is planned obsolescence , when a product has been designed to have a limited lifespan to encourage consumers to buy it again. The European Parliament has called for measures to tackle this practice.

Infographic explaining the circular economy model

Benefits: why do we need to switch to a circular economy?

To protect the environment.

Reusing and recycling products would slow down the use of natural resources, reduce landscape and habitat disruption and help to limit biodiversity loss .

Another benefit from the circular economy is a reduction in total annual greenhouse gas emissions . According to the European Environment Agency, industrial processes and product use are responsible for 9.10% of greenhouse gas emissions in the EU, while the management of waste accounts for 3.32%.

Creating more efficient and sustainable products from the start would help to reduce energy and resource consumption, as it is estimated that more than 80% of a product's environmental impact is determined during the design phase.

A shift to more reliable products that can be reused, upgraded and repaired would reduce the amount of waste. Packaging is a growing issue and, on average, the average European generates nearly 180 kilos of packaging waste per year . The aim is to tackle excessive packaging and improve its design to promote reuse and recycling.

Reduce raw material dependence

The world's population is growing and with it the demand for raw materials. However, the supply of crucial raw materials is limited.

Finite supplies also means some EU countries are dependent on other countries for their raw materials. According to Eurostat , the EU imports about half of the raw materials it consumes.

The total value of trade (import plus exports) of raw materials between the EU and the rest of the world has almost tripled since 2002, with exports growing faster than imports. Regardless, the EU still imports more than it exports. In 2021, this resulted in a trade deficit of €35.5 billion.

Recycling raw materials mitigates the risks associated with supply, such as price volatility, availability and import dependency.

This especially applies to critical raw materials , needed for the production of technologies that are crucial for achieving climate goals, such as batteries and electric engines.

Create jobs and save consumers money

Moving towards a more circular economy could increase competitiveness, stimulate innovation, boost economic growth and create jobs ( 700,000 jobs in the EU alone by 2030 ).

Redesigning materials and products for circular use would also boost innovation across different sectors of the economy.

Consumers will be provided with more durable and innovative products that will increase the quality of life and save them money in the long term.

What is the EU doing to become a circular economy?

  In March 2020, the European Commission presented the circular economy action plan,  which aims to promote more sustainable product design, reduce waste and empower consumers, for example by creating a right to repair ). There is a focus on resource intensive sectors, such as electronics and ICT , plastics , textiles and construction.

In February 2021, the Parliament adopted a resolution on the new circular economy action plan demanding additional measures to achieve a carbon-neutral, environmentally sustainable, toxic-free and fully circular economy by 2050, including tighter recycling rules and binding targets for materials use and consumption by 2030. In March 2022, the Commission released the first package of measures to speed up the transition towards a circular economy, as part of the circular economy action plan. The proposals include boosting sustainable products, empowering consumers for the green transition, reviewing construction product regulation, and creating a strategy on sustainable textiles.

In November 2022, the Commission proposed new EU-wide rules on packaging . It aims to reduce packaging waste and improve packaging design, with for example clear labelling to promote reuse and recycling; and calls for a transition to bio-based, biodegradable and compostable plastics.

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    1. Introduction. Circular Economy (CE) emerged in the 1970s from the idea of reducing the consumption of inputs for industrial production, but it proves to be potentially applicable to any resource [23].Through the possibility of making human activity more resilient, using the natural cycle model, CE proposes a change in the "extraction-production-disposal" paradigm of linear economy (LE ...

  16. PDF How to Write a Research Paper in Economics

    Economic questions ask: 1 How would an individual/group solve a particular problem, or respond to a particular incentive? E.g. If the interest rate on a savings account increases by 5%, how much more would an individual save per month? OR 2 How would a particular variable respond to an exogenous shock? E.g.

  17. The power of 'geoeconomics' to make sense of a turbulent world

    A new paper by Stanford economist Matteo Maggiori offers policymakers a framework for understanding how economic power is used to achieve geopolitical goals. ... it soon became dormant in economic research, partly due to the lack of the theoretical tools to properly characterize the complexities involved.

  18. Research

    Take a look at the latest research from MIT Economics faculty, including published work and newly-released working papers. Working Papers All Working Papers. Econometrics, Macroeconomics ... using economic science to help tackle the complex issues surrounding global poverty, health care, education, and more. ...

  19. Gender inequality as a barrier to economic growth: a review of the

    The vast majority of theories reviewed argue that gender inequality is a barrier to economic development, particularly over the long run. The focus on long-run supply-side models reflects a recent effort by growth theorists to incorporate two stylized facts of economic development in the last two centuries: (i) a strong positive association between gender equality and income per capita (Fig. 1 ...

  20. The impact of entrepreneurship on economic, social and ...

    This paper presents a systematic review of (a) the impact of entrepreneurship on economic, social and environmental welfare and (b) the factors determining this impact. Research over the past 25 years shows that entrepreneurship is one cause of macroeconomic development, but that the relationship between entrepreneurship and welfare is very complex. The literature emphasizes that the generally ...

  21. Research in Economics

    Established in 1947, Research in Economics is one of the oldest general-interest economics journals in the world and the main one among those based in Italy. The purpose of the journal is to select original theoretical and empirical articles that will have high impact on the debate in the social … View full aims & scope $2740

  22. Economics and Finance Research

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  23. Technology and the Innovation Economy

    Executive Summary. Innovation and entrepreneurship are crucial for long-term economic development. Over the years, America's well-being has been furthered by science and technology. Fears set ...

  24. Opinion

    Ms. Cramer is a co-chair of the Commission on Reimagining Our Economy at the American Academy of Arts & Sciences. Mr. Cohen is a senior program officer at the American Academy of Arts & Sciences ...

  25. (PDF) Future of Gig Economy: Opportunities and Challenges

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  26. (PDF) On the Impact of Artificial Intelligence on Economy

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  27. Data, Privacy Laws and Firm Production: Evidence from the GDPR

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  28. Circular economy: definition, importance and benefits

    The circular economy is a model of production and consumption, which involves sharing, leasing, reusing, repairing, refurbishing and recycling existing materials and products as long as possible. In this way, the life cycle of products is extended. In practice, it implies reducing waste to a minimum. When a product reaches the end of its life ...