Demographics and Innovation€¦ · Demographics and Innovation François Derrien Ambrus Kecskés...
Transcript of Demographics and Innovation€¦ · Demographics and Innovation François Derrien Ambrus Kecskés...
Demographics and Innovation
François Derrien
Ambrus Kecskés
Phuong-Anh Nguyen
Workshop on Technology and Aging Workforce
Seoul, May 2018
Motivation
• Population is aging consequences for the economy?
• Younger people have characteristics that are crucial for innovation
– Risk taking
– Longer investment horizons
– Creativity
– Interactivity
• Young labor force Innovation Growth
• Question: Are younger labor forces more innovative?
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This paper
• Link between age structure of the local labor force and innovation
• Mechanism: Labor supply
– In younger areas, firms hire from a larger pool of young employees
More innovative inventors and non-inventors
More innovative firms
• To disentangle this from other channels: Three levels of analysis
– Commuting zone
• Most general, to establish and quantify the link
• Where people live and work (commonly used in labor economics)
– Firm
• Allows to test for alternative channels
– Inventor
• Allows to explore interaction effects
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Our approach
• Actual population is endogenous to contemporaneous economic
activity
• People migrate because of contemporaneous economic conditions
• Actual population = Native-born population + (Immigration –
Emigration)
• Native-born population
– Projected population using historical births
– Births 20-64 years before are plausibly exogenous to economic activity
(e.g., innovation) decades later
Use this to estimate the causal link between population age structure and
innovation
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Measurement of age structure
• For example, in 1990
– Look back 20-64 years to 1926-1970
• Collect births each year, adjust for survival
• This is the native-born labor force in 1990
• Calculate measures of age structure in 1990
– mean age
– young share
• = population aged 20 to 39 / Population aged 20 to 64
• Repeat i 99 , 99 , …, 5
– Stop in 2005 because patent data end in 2010
– Really just 1990, 1995, 2000, and 2005
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Evolution of the mean age of the projected and
actual labor forces by location, 1990-2005
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Native born labor force
• Similar age structures for actual and projected labor forces
– Correlation of about 0.5
• Different location, different time trends
• But: Remove time trends, not much time-series variation left
We identify off cross-sectional variation
– Want to absorb sources of common variation in innovation and age
structure Use many FEs and control variables
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Our methodology and main finding
Illustrating figure – year 2000
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What if we use actual instead of projected
age of the labor force?
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Commuting-zone-level tests
• Specification
Innovationc,s,t+1 = α.age structurec,t + β.controlsc,t + αs,t
• Controls
– Possible source of endogeneity: Omitted variables
– Correlated with innovation today and births 20-64 years ago
• Variables related to economic conditions
– Population (scale effects in innovation, urban vs. rural areas)
– Income per capita (wealth)
– Growth rate of total income (growth)
• Long-term consequences of past investments (public or private)
– Local government expenditures
– Edu atio % o er 5 ith a helor’s degree or higher
– Patents of local university
– Variables measured at date t
• Results robust to averaging them over the last 20 years
• Clustering at the state-year level
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Commuting-zone-level tests – Results
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A one-standard deviation change in mean age (2.3 years)
leads to a 25-30% increase in patents
Firm-level tests
• Specification
Innovationi,j,a,c,s,t+1 = α.age structurec,t + β.controlsi,t + αs,t + αj,t + αa
• Age structure: in The CZ where the firm is headquartered
• Allows us to study
– Industry composition effects (innovative industries locate in younger areas): αj,t
– Firm life cycle effects (younger firms locate in younger areas): αa
– Alternative channels
• Financing supply
• Consumer demand
• Controls
– Standard corporate finance controls Listed firms only
• Robust to including CZ-level controls
• Firm location
– Using headquarter location from Compustat
– Robust to using historical HQ location or location at IPO date
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Firm-level tests - Results
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Alternative mechanisms
• Financing supply channel
– Younger areas have younger investors, who prefer equity
Easier to obtain financing for innovative firms
– Look at the link between innovation and age structure
• At the fir ’s HQ, here the fir o tai s is fi a i g
• At the fir ’s R&D hu s, here it produ es its i o atio
– Stronger results at R&D hubs
• Consumer demand channel
– Younger areas have younger consumers, who prefer innovative products
Local firms more innovative in younger areas
– Compare firms in non-tradable industries (in which production and
consumption happen at the same place) vs. tradable industries (in which they
do ’t
– Results only for tradable industries
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Inventor-level tests
• Specification
Innovationi,j,k,c,s,t+1 = α.age structurec,t + β.controlsi,k,t + αs,t + αj,t + αi
• Focus on stars (top 5% based on patent counts in prior 10 years)
– Dominate production of innovation (majority of patent counts and citations)
– Data sparse for non-stars
• Controls
– Inventor level
• I e tor’s age si e first pate t , pate t sto k
– Firm level
• Firm age (since first patent), patent stock, # of inventors at the same R&D hub, total # of
inventors, total # of R&D hubs
• Less precise than in firm-level tests because we use data from private firms
• But we use firm FEs (αi)
• Allows us to study separately
– The effe t of i e tor’s age s. fir age
– The effect of i e tor’s age s. the age structure of the environment
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Inventor-level tests - Results
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Value implications
• Younger labor forces help firms create valuable growth
opportunities
• Market prices should reflect these growth opportunities
• Age structure Firm valuations
– Use the Market-to-Book of (public) firms as a measure of valuation
– Same specification as in firm-level tests
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Value implications
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Summary
• Local age structure affects innovation
– At the CZ level
– At the firm level
– At the inventor level
• Labor supply channel
– Firms in younger locations are able to hire younger, more
innovative employees, which enables them to produce more
innovation
• This affects the long-term growth opportunities of firms
and therefore their value
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