Presentation by John Haltiwanger, Professor of Economics, University of Maryland, USA at the 17th OECD Spatial Productivity Lab webinar held on 14 September 2022. More info https://oe.cd/spl

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