15.2 Technological Progress and Productivity

Technological progress and productivity growth are key drivers of economic development. They enhance production efficiency, create new opportunities, and disrupt existing industries through innovations. Understanding these concepts is crucial for grasping how economies evolve and grow over time.

Measuring productivity, analyzing R&D processes, and examining innovation dynamics provide insights into economic growth mechanisms. These factors shape labor markets, influence inequality, and present societal challenges, making them essential considerations in modern economic policy and business strategy.

Technology and Productivity

Technological Progress and Productivity Growth

Top images from around the web for Technological Progress and Productivity Growth

• 

  Information Transfer Economics: The Solow growth model and information transfer View original

  Is this image relevant?

• 

  File:Solow growth model3.png - Wikimedia Commons View original

  Is this image relevant?

• 

  Which investments in human capital will boost productivity growth? - Economics Observatory View original

  Is this image relevant?

• 

  Information Transfer Economics: The Solow growth model and information transfer View original

  Is this image relevant?

• 

  File:Solow growth model3.png - Wikimedia Commons View original

  Is this image relevant?

1 of 3

Top images from around the web for Technological Progress and Productivity Growth

• 

  Information Transfer Economics: The Solow growth model and information transfer View original

  Is this image relevant?

• 

  File:Solow growth model3.png - Wikimedia Commons View original

  Is this image relevant?

• 

  Which investments in human capital will boost productivity growth? - Economics Observatory View original

  Is this image relevant?

• 

  Information Transfer Economics: The Solow growth model and information transfer View original

  Is this image relevant?

• 

  File:Solow growth model3.png - Wikimedia Commons View original

  Is this image relevant?

1 of 3

• Technological progress advances knowledge, techniques, and tools enhancing production efficiency and product quality
• Productivity growth measures increased output per input unit (labor or total factor productivity) over time
• Solow growth model shows long-term economic growth driven primarily by technological progress rather than capital accumulation or labor force growth
• Process innovations improve production methods while product innovations create new or improved goods and services
• Creative destruction, introduced by Joseph Schumpeter, explains how technological progress disrupts existing industries while creating new growth opportunities
  • Examples: Automobiles disrupting horse-drawn carriages, digital cameras replacing film cameras
• Endogenous growth theory posits technological progress results from deliberate investments in human capital, knowledge, and innovation
  • Examples: Government funding for education, corporate R&D spending

Measuring and Analyzing Productivity

• Productivity measured as output per unit of input
  • Labor productivity: output per worker or per hour worked
  • Total factor productivity: output relative to all inputs (labor, capital, materials)
• Productivity growth calculation: $\text{Productivity Growth} = \frac{\text{Output Growth} - \text{Input Growth}}{\text{Input Growth}} \times 100\%$
• Factors affecting productivity growth:
  • Technological advancements (robotics, artificial intelligence)
  • Human capital development (education, training)
  • Organizational improvements (lean manufacturing, agile methodologies)
  • Resource allocation efficiency (shift from low to high-productivity sectors)
• Productivity measurement challenges:
  • Quality improvements not fully captured in output measures
  • Difficulty in measuring intangible inputs (knowledge, organizational capital)
  • Time lags between technology adoption and productivity gains

R&D for Innovation

R&D Process and Models

• Research and Development (R&D) discovers new knowledge and applies it to create or improve products, processes, or services
• Linear model of innovation: basic research → applied research → development → commercialization
  • Example: Discovery of DNA structure leading to genetic engineering applications
• Non-linear innovation models:
  • Chain-linked model: emphasizes feedback loops and interactions between different stages
  • Open innovation: leverages external sources of knowledge and collaboration
    • Example: Procter & Gamble's Connect + Develop program
• R&D expenditures serve as a proxy for measuring innovation commitment
  • Measured as percentage of GDP for countries or percentage of revenue for firms
• Government funding crucial for long-term technological advancements without immediate commercial applications
  • Examples: DARPA's role in developing the internet, NASA's space exploration technologies

R&D Spillovers and Market Dynamics

• Spillover effects explain how R&D efforts in one sector benefit innovations in others
  • Example: Aerospace research leading to advancements in materials science
• Market structure impacts R&D investment:
  • Monopolistic markets: Potential for higher R&D investment due to greater resources and market power
  • Competitive markets: Innovation as a means of gaining competitive advantage
• Intellectual property rights (patents, copyrights) incentivize private R&D investment
  • Allow firms to temporarily capture returns from innovations
  • Balance between innovation incentives and knowledge diffusion
• Challenges in R&D:
  • High costs and uncertain outcomes
  • Difficulty in appropriating returns from basic research
  • Potential for duplication of efforts across firms or sectors

Innovation and Economic Growth

Entrepreneurship and Innovation Dynamics

• Innovation implements new ideas, products, or processes; entrepreneurship identifies opportunities and takes risks to bring innovations to market
• Schumpeterian growth theory emphasizes entrepreneurs as agents of creative destruction driving economic growth
• Disruptive innovation creates new markets and value networks, displacing established market leaders
  • Examples: Personal computers disrupting mainframes, smartphones disrupting various industries
• Entrepreneurial ecosystems foster innovation-driven growth:
  • Access to venture capital and angel investors
  • Supportive regulatory environments (ease of starting businesses, bankruptcy laws)
  • Knowledge networks (universities, research institutions, industry clusters)
• Diffusion of innovations theory describes how new ideas and technologies spread through society
  • Adopter categories: innovators, early adopters, early majority, late majority, laggards
• High-growth entrepreneurial firms ("gazelles") contribute disproportionately to job creation and economic growth
  • Examples: Tech startups like Uber, Airbnb rapidly scaling and creating new markets

Innovation Impact and Measurement

• Relationship between innovation, entrepreneurship, and productivity growth often non-linear
  • Time lags between technology introduction and aggregate productivity impact
  • Example: Productivity paradox in the 1980s with IT investments
• Innovation measurement metrics:
  • Input measures: R&D spending, number of researchers
  • Output measures: Patents, scientific publications
  • Impact measures: New product sales, productivity growth
• National innovation systems framework analyzes interactions between institutions, policies, and actors in fostering innovation
  • Examples: Silicon Valley in the US, Shenzhen in China
• Global Innovation Index ranks countries based on innovation capabilities and results
• Challenges in measuring innovation:
  • Capturing incremental vs. radical innovations
  • Accounting for non-technological innovations (business model, organizational)
  • Measuring innovation in services and intangible-intensive sectors

Challenges of Technological Change

Labor Market Impacts and Inequality

• Skill-biased technological change favors skilled over unskilled labor, potentially leading to wage inequality and structural unemployment
  • Example: Automation in manufacturing displacing low-skilled workers
• Technological unemployment occurs when new technologies displace workers faster than job creation
  • Requires policies for retraining and education to mitigate negative impacts
  • Examples: Massive Open Online Courses (MOOCs), coding bootcamps
• "Digital divide" refers to unequal access to technology and digital skills
  • Exacerbates economic inequalities between regions, countries, and socioeconomic groups
  • Examples: Rural-urban divide in broadband access, global disparities in smartphone ownership
• Gig economy and platform work create new employment patterns
  • Benefits: Flexibility, lower barriers to entry
  • Challenges: Job security, benefits, worker classification
  • Examples: Uber drivers, Upwork freelancers

Societal and Ethical Considerations

• Technological change presents opportunities for addressing global challenges
  • Climate change: Renewable energy technologies, carbon capture
  • Healthcare: Telemedicine, personalized medicine based on genetic data
  • Education: Adaptive learning platforms, virtual and augmented reality in classrooms
• Ethical considerations surrounding technological change require careful policy approaches:
  • Privacy concerns: Data collection and use by tech companies and governments
  • Algorithmic bias: Potential discrimination in AI-driven decision-making systems
  • Societal impacts of automation: Job displacement, social safety nets
• Productivity paradox highlights challenges in measuring productivity gains from technological advancements
  • Particularly evident in service sector and with intangible assets
  • Examples: Improved quality of products not fully captured in GDP measurements
• Regulatory challenges in the digital age:
  • Antitrust concerns with big tech companies
  • Cybersecurity and data protection regulations
  • Taxation of digital services and multinational tech firms
• Balancing innovation and precaution in emerging technologies:
  • Gene editing (CRISPR technology)
  • Artificial General Intelligence development
  • Autonomous vehicles and drones