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Business Dynamism and Entrepreneurship: Changing Patterns in 21stCentury - John Haltiwanger

  1. Business Dynamism and Entrepreneurship: Changing Patterns in 21st Century By John Haltiwanger, University of Maryland September 2022
  2. Overview • Much evidence that new employer startups contribute disproportionately to job creation, innovation and productivity growth • Entrepreneurs inherently induce and drawn to innovation. • Play a critical role in experimentation • Pre-pandemic: • Declining productivity, entrepreneurship and dynamism in post 2000 period. • Rising concentration and markups as well? • The pandemic has led to a surprising surge in applications for new businesses • Patterns suggest spatial and sectoral reallocation induced by pandemic • Implications for productivity ?
  3. Most young firms fail or don’t grow. A small fraction grow rapidly 0 2 4 6 8 10 12 14 16 18 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16+ Percent of employment Firm age Job destruction from exiting firms Net job creation of continuing firms -60 -40 -20 0 20 40 60 80 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16+ Employment growth rate (DHS denominator) Firm age Median 90th percentile Source: Decker, Haltiwanger, Jarmin and Miranda (2014) Skewness much greater in innovative-intensive Industries.
  4. Dynamics of Entry, Productivity dispersion and Productivity growth 4 -0.01 -0.005 0 0.005 0.01 0.015 Years 4-6 Years 7-9 Changes in Productivity Dispersion and Growth from a 1% (one time) Increase in Entry Rate (Years 1-3), High Tech Dispersion(High Tech) Growth(High Tech) Surge in entry in a given 3-year period leads to: • Rise in within industry productivity dispersion and decline in industry productivity growth in next 3-year Period • Decline in within industry productivity dispersion and rise in industry in subsequent 3-year period • Surge in reallocation following surge in entry as well (not depicted). • Similar, dampened patterns for Non-Tech Source: Foster et. al. (2018) Using 4-digit NAICS data for High Tech sectors (ICT in mfg and non-mfg plus sectors such as Bio Tech)
  5. Young firms are more innovative intensive 5 0 1 2 3 4 5 6 7 Small, Young Large, Mature R&D to Sales (x100) Innovation Intensities by Firm Size and Age Source: Acemoglu et. al. (2018)
  6. 0 2 4 6 8 10 1947-73 1974-94 1995-2004 2005-2010 2011-2013 2014-2018 Growth Rate in TFP and Output Per Hour, Business Sector (Average Annual) Total Factor Productivity (Util Adj) Output Per Hour 0 2 4 6 8 10 1990-1994 1995-2004 2005-2010 2011-2013 2014-2018 Growth Rate in Output Per Hour (Average Annual) Non Tech Tech All Source: Left Panel from Fernald, SF Fed. Right Panel from Aggregated 4-digit industries from BLS Surge and Slowdown in Productivity dominated by High Tech (ICT) . Young Firm dynamics Distinct for ICT compared to overall economy.
  7. Declining Dynamism and Rising Markups Left panel from the BDS. Right panel from De Loecker et. al. (2020)
  8. Declining entrepreneurship especially on employment-weighted basis. Even in High Tech post 2000. Increased concentration of activity in High-Tech (Non-Mfg) in Mega Firms Source: Business Dynamic Statistics (BDS)
  9. Consistent with theory, businesses with positive shocks grow and are more likely to survive. There is also evidence of changing responsiveness. We will return to that later Source: Decker et. al. (2020) using tabulations from LBD/ASM/CM 0 2 4 6 8 10 12 14 Percentage points Growth Exit (inverse) a. Manufacturing (TFPS) 1980s 1990s 2000s 0 5 10 15 20 25 Percentage points Growth Exit (inverse) b. Economywide (RLP) 1996-99 2011-13 Note: Compares employment growth rate or (inverse) exit probability of establishment or firm that is one standard deviation above its industry-year mean productivity, versus the mean. Source: ASM-CM (panel a); RE-LBD (panel b). Figure 4: Job growth and exit have become less responsive to productivity
  10. A turning point – the pandemic? • Early in pandemic new business applications from BFS for likely employer business startups fell sharply • But surprisingly: New business applications have surged since June 2020 • 2020-22 is highest on record • Applications remain at historical highs through July 2022 • Patterns consistent with spatial and
  11. New Business Applications: BA=All new business Applications HBA=Likely New Employers NHBA=Likely New Nonemployers 1. BA highly predictive of actual employer startups 2. NHBA predictive of new nonemployer businesses. 3. Leading indicator of Reallocation. Source: Tabulations from BFS. Source: BFS. Next several slides from Decker and Haltiwanger (2022).
  12. Large increase In dispersion in 3-digit Net Growth Rates Across Years Source: BFS
  13. Log Differences in Applications Per (1000) Capita Between Pre-Pandemic (2010-19) and Pandemic (2020-21). Top counties increase by 52 log points up to 275 log points. Tremendous differences across areas. Source: BFS
  14. Log Difference in Applications Per Capita Pandemic and Pre-Pandemic. Adjacent or Close by counties From Manhattan in NY experience Growth Compared to Manhattan But also outlying counties Part of the story is a movement away From Center Cities NYC CBSA Manhattan Source: BFS
  15. Surge in Applications is leading to a surge in new employer businesses Source: BFS and BED.
  16. There has been rapid NET growth in establishments in the pandemic. Source: BLS Quarterly Census of Employment and Wages (QCEW)
  17. Growth is measured By log differences Of Measures between Pandemic (2020-21) And Pre-Pandemic (2010-19). Caution: Net establishment Growth are for Employer Businesses And Applications are all. Also recall lags from Applications to startups. Binscatter plot of log differences from County Variation Source: QCEW
  18. Log Differences in Establishments Per Capita Between Pre- Pandemic (2010-19) and Pandemic (2020-21). Net entry of establishments similar to Growth in Applications Per Capita around NYC Source: QCEW