The document analyzes the effects of housing prices and credit supply on young firm activity using panel data at the metropolitan statistical area (MSA) level from 1981-2014. The key findings are: 1) Using an instrumental variables approach, the study finds large effects of local house price changes on local young firm employment growth and shares. 2) A separate, smaller role is found for locally exogenous shifts in bank lending supply on young firm activity. 3) Housing market fluctuations play a major role in driving medium-run fluctuations in young firm employment shares by acting as a transmission channel and driving force in recent decades according to the analysis.

1. Dynamism Diminished: The Role of
Housing Markets and Credit Conditions
Steven J. Davis
Research with John Haltiwanger
OECD Global Productivity Forum
Sydney, 20-21 June 2019

2. The Great Recession and its aftermath saw the worst relative performance of young
firms in at least 35 years. More broadly, as we show, young-firm activity shares move
strongly with local economic conditions and local house price growth. In this light, we
assess the effects of housing prices and credit supply on young-firm activity. Our panel
IV estimation on MSA-level data yields large effects of local house price changes on local
young-firm employment growth and employment shares and a separate, smaller role for
locally exogenous shifts in bank lending supply. A novel test shows that house-price
effects work through wealth, liquidity and collateral effects on the propensity to start
new firms and expand young ones. Aggregating local effects to the national level,
housing market ups and downs play a major role – as transmission channel and driving
force – in medium-run fluctuations in young-firm employment shares in recent decades.
The great housing bust after 2006 largely drove the cyclical collapse of young-firm
activity during the Great Recession, reinforced by a contraction in bank loan supply. As
we also show, when the young-firm activity share falls (rises), local employment shifts
strongly away from (towards) younger and less-educated workers.
Abstract

3. Some Context
and Motivation
3

4. 4
Share of Employees in Young Firms, 1981-2014,
U.S. Nonfarm Private Sector
Source: Annual Rates, Business
Dynamic Statistics (BDS)
Employment in firms less than
five years old fell from about 18% of
private sector employment in 1981
and 1987-1988 to 9% in 2014.
“Young” means < 60 months since the firm’s first paid
employee as of March in the indicated calendar year.
“Firm age” is set to age of its oldest establishment when the firm first becomes a legal entity,
and increments by 1 each year thereafter. Establishment age is the number of years since
operations began at the location in the same narrowly defined industry.
6
8
10
12
14
16
18
20
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
Percent

5. 5
The Young-Firm Share of Employment Exhibits
Pronounced and Time Varying Cyclicality
For each expansion and contraction episode, the chart shows annualized
deviations from the overall mean, which equals -2.2 log points per year.
(NOTE: Timing convention is that change in year t represents the change from March t-1 to March t).
-5
-4
-3
-2
-1
0
1
2
3
LogPoints

6. Young Firm Outcomes at the
State-Year Level,
Using Data from 1981 to 2014
6

7. 7
Simple bivariate relationships show great
variation across states and time.
Log Difference in Young Firm Employment Share, Unemployment Rates and Housing
Prices in State-by-Year Data from 1981 to 2014
Employment share of young firms declines
when local economic conditions deteriorate
(as measured by local unemployment rate).
Employment share of young firms rises when local
housing prices increase
-50
0
50
-4 -2 0 2 4 6
Change in Unemployment
Fitted 1980-83 1983-90 1990-91 1991-01
2001-03 2003-08 2008-10 2010-14
Slope = -1.8153, SE = 0.1277
-50
0
50
LogDiffofYoungEmpShare
-40 -20 0 20 40
Growth in Real Housing Price
Fitted 1980-83 1983-90 1990-91 1991-01
2001-03 2003-08 2008-10 2010-14
Slope = 0.3021, SE = 0.0239

8. Our Main Data Sources
• Business Dynamic Statistics (BDS): Annual activity for firms with paid employees,
with tabulations by firm age at State and MSA level.
• Quarterly Workforce Indicators (QWI): Similar to BDS in key respects, but includes
tabs by age-industry-MSA (including by gender-worker age-education). Covers
shorter time period than BDS.
• Local area unemployment rates (LAUS): This BLS program uses CPS data, UI claims,
CES data and other sources to estimate local unemployment rates.
• Local house price measures (FHFA): Federal Home Loan Finance Agency data,
available monthly at MSA and State Level.
• Saiz MSA housing supply elasticity: To instrument for house price changes
• Local bank loan supply (CRA): Community Reinvestment Act data. Banks with assets
>1 billion report # and volume of loans to businesses with <$1 million in gross
revenue. We use to construct local “small business” loan supply shocks.
• Quarterly Census of Employment and Wages (QCEW): Employment at MSA-industry
level. To construct Bartik-like instruments for local cycle variables. 8

9. Identification and Estimation
9

10. Identification
• Instrument local house price changes to identify effects on local young-firm
activity shares. Two IV approaches that rely on distinct variation:
1. IV(1) Exploit national housing boom & bust episodes that affect MSA-level house
prices differently due to differences in local housing supply elasticities
• Follows Mian and Sufi, except we stack boom and bust changes and consider different
outcomes. Use Saiz housing supply elasticity instruments.
• Stacking permits controlling for MSA specific trends.
2. IV(2) Exploit local area demand shifts interacted with local housing supply elasticities
to instrument for local house price changes.
3. We also include additional local controls – omitted variable bias may yield violation
of exclusion restriction even using Saiz instruments.
• Supplement with local “small” business loan shocks by adapting approach of
Greenstone, Mas and Ngyugen (2015).
• Fits well with IV approach 2 above.
• A shorter times-series dimension, because CRA data are only available from late 1990s.10

11. IV Approach (1): Stacked Boom/Bust Episodes
𝑌 𝑚𝑠 = σ 𝑠 𝜆 𝑠 𝐼𝑠 + σ 𝑚 𝜆 𝑚 𝐼 𝑚 + 𝛽𝐻𝑃𝑚𝑠 + 𝜀 𝑚𝑠 (1) Second Stage
𝐻𝑃𝑚𝑠 = σ 𝑚 𝛿 𝑚 𝐼 𝑚 + σ 𝑠 𝛿𝑠 𝐼𝑠 + σ 𝑠 𝑍 𝑚 𝐼𝑠 𝛾𝑠 + 𝜂 𝑚𝑠 (2) First Stage
𝑌 𝑚𝑠 = log change in MSA young-firm employment share
𝐻𝑃𝑚𝑠 = log change in MSA house price index
𝐼𝑠 is dummy for period s, and 𝐼 𝑚 is dummy for MSA m
𝑍 𝑚 is cubic in Saiz housing supply elasticity
𝜆𝑖 and 𝛿𝑖 are coefficients on dummy variables
Exclusion restriction: 𝐶𝑜𝑣 𝑍 𝑚 𝐼𝑠, 𝜀 𝑚𝑠 = 0. That is, 𝑍 𝑚 𝐼𝑠 influences young-firm share only
through house price growth, conditional on period and MSA effects in the first equation.
466 observations: 233 Boom changes (2002-2006) + 233 Bust changes (2007-2010).
Stacking boom and bust episodes lets us control for arbitrary differences in MSA-level trends
in the 2000s, addressing concerns that these trends are correlated with MSA-level housing
supply elasticities, as argued by Davidoff (2015). 11

12. 12
Bivariate MSA-
Level Relationship
of House Price
Growth to Log
Change in Young-
Firm Share in
Boom, Bust and
Other Periods
All three panels show
annualized log changes

13. 13
(1) (2) (3) (4)
OLS
(Boom/Bust)
IV
(Boom/Bust)
OLS
(Boom/Bust)
IV
(Boom/Bust)
Growth in real
housing price
0.171***
(0.040)
0.190***
(0.070)
0.184***
(0.049)
0.194***
(0.057)
F-Test for Excluded
Instruments
31.4 35.3
Period Effects Yes Yes Yes Yes
MSA Effects No No Yes Yes
R2 0.247 0.247 0.515 0.515
Observations 466 466 466 466
Response of Log Difference in Young-Firm Share to Housing Price Growth,
IV Approach (1): Stacked Boom/Bust
Notes: Boom (2002-06), Bust (2007-10). Instruments are period effects interacted
with cubic in (log) Saiz elasticity. Standard errors in parentheses clustered at MSA
level. 466 observations in each specification.
Controlling for
Differential MSA-
Level Trends

14. 14
The specification includes
period controls. See Column
(2) in the table for standard
errors that are adjusted for
the two-stage nature of
the estimation.
Second-Stage Relationship between Log Change in Young-Firm Share
and House Price Growth – Column 2 in the Previous Table
Bust
Boom

15. IV Approach (1): On Identification
1. Measurement Error: IV addresses concerns that OLS yields a
(downwardly) biased estimate of 𝛽 due to measurement error in
HP – a serious concern, in our view, given the difficulties of
constructing good house price indices.
2. Reverse causality: Not a serious concern in our view, given that
our lhs variable is log change in the young-firm employment
share. In other words, we do not think exogenous shocks to the
local young-firm share drive changes in local house price growth.
The small size of the young-firm share also limits concerns about
reverse causality.
3. Omitted Variables: Our specifications might not adequately
control for local cycle conditions that affect local house price
growth and 𝑌 𝑚𝑠 -- a serious concern in our view. 15

16. IV Approach (1): On Identification
Omitted variables can cause the 𝐶𝑜𝑣 𝑍 𝑚 𝐼𝑠, 𝜀 𝑚𝑠 = 0 assumption to fail. To
see how, suppose the true specification is given by:
𝑌 𝑚𝑠 = σ 𝑠 𝜆 𝑠 𝐼𝑠 + σ 𝑚 𝜆 𝑚 𝐼 𝑚 + 𝛽𝐻𝑃𝑚𝑠 + 𝛼𝑋 𝑚𝑠 +𝜀 𝑚𝑠 (1)’
𝐻𝑃𝑚𝑠 = σ 𝑚 𝛿 𝑚 𝐼 𝑚 + σ 𝑠 𝛿𝑠 𝐼𝑠 + σ 𝑠 𝑍 𝑚 𝐼𝑠 𝛾𝑠 + 𝜃𝑋 𝑚𝑠 + 𝜂 𝑚𝑠 (2)’
Where 𝑋 𝑚𝑠 is a local shock that affects the young-firm activity share in (1)’.
Such local shocks may also affect local house prices in (2)’. Suppose further
that 𝐶𝑜𝑣 𝑍 𝑚 𝐼𝑠, 𝑋 𝑚𝑠 ≠ 0, i.e., that the local shock 𝑋 𝑚𝑠 is also correlated with
our instrument. In this case, estimating (1) rather than (1)’ will violate the
assumption that 𝐶𝑜𝑣 𝑍 𝑚 𝐼𝑠, 𝜀 𝑚𝑠 = 0. This limitation can be overcome by
estimating (1)’ rather than (1) along with (2)’ rather than (2).
16

17. IV Approach (1): On Identification
The next slide considers the sensitivity of the key coefficient of interest to the
inclusion of several local shock control variables:
• Local cycle control: The average annualized change in the MSA-level
unemployment rate during the period.
• Bartik-type local demand shifter: The average annualized employment
growth rate implied by (lagged MSA-level industry employment share) X
(national industry employment growth) summed over all industries at the 2-
digit NAICS level. Results are similar using 4-digit NAICS data, but there is
much suppression of cell-level data at 4-digit NAICS by MSA level.
• Local population growth: The average annualized change in the MSA-level
population during the period.
17

18. 18
(1) (2) (3) (4)
IV IV IV IV
Growth in real housing
price
0.194***
(0.057)
0.174**
(0.075)
0.173**
(0.081)
0.161**
(0.079)
F-Test for Excluded
Instruments
26.9 26.7 26.0 23.4
Period & MSA Effects Yes Yes Yes Yes
MSA Unemp. Rate Change No Yes Yes Yes
MSA Bartik Shock No No Yes Yes
MSA Population Growth No No No Yes
R2 0.515 0.519 0.520 0.522
Response of Log Difference in Young-Firm Share to Housing Price Growth,
IV Approach (1): Stacked Boom/Bust Episodes with Additional Controls
Notes: Boom (2002-06), Bust (2007-10). Instruments are period effects interacted with cubic
in (log) Saiz elasticity. Standard errors in parentheses clustered at MSA level. 466
observations in each specification.
Same as column (4)
in previous table

19. IV(2) Approach: On Identification
𝑌 𝑚𝑡 = ෍
𝑡
𝜆 𝑡 𝐼𝑡 + ෍
𝑚
𝜆 𝑚 𝐼 𝑚 + 𝛽𝐻𝑃 𝑚𝑡 + 𝛼𝐶𝑌𝐶 𝑚𝑡 + 𝑋 𝑚𝑡
′
Α +𝜀 𝑚𝑡
𝐻𝑃 𝑚𝑡 = σ 𝑚 𝛿 𝑚 𝐼 𝑚 + σ 𝑠 𝛿𝑡 𝐼𝑡 + 𝐶𝑌𝐶 𝑚𝑡 𝑍 𝑚
′ Γ + 𝛼𝐶𝑌𝐶 𝑚𝑡 + 𝑋 𝑚𝑡
′
Β + 𝜂 𝑚𝑡
𝑌 𝑚𝑡 = log change in MSA young-firm employment share
𝐻𝑃 𝑚𝑡 = log change in MSA house price index
𝐼𝑡 is dummy for period t, and 𝐼 𝑚 is dummy for MSA m
𝑍 𝑚 is cubic in Saiz housing supply elasticity
𝑋 𝑚𝑡 is a vector of local controls (e.g., Bartik local demand shifter and population controls).
𝐶𝑜𝑣(𝐶𝑌𝐶 𝑚𝑡 𝑍 𝑚, 𝜀 𝑚𝑡) = 0); i.e., the interaction between local cycle and local supply
elasticity affects 𝑌 𝑚𝑡 only through its effect on local house price growth, 𝐻𝑃 𝑚𝑡, conditional
on controls.
IV addresses measurement error and endogeneity in the house price index. MSA effects
and local controls address unobserved MSA trends and omitted variable bias (related to
concerns by Davidoff (2015). 19

20. 20
IV Estimates for 1992-2014 Sample, IV Approach (2)
Dependent Variable: Log Change in Young-Firm Employment Share, MSA by Year Data
Notes: Standard errors in parentheses clustered at MSA level. All specs include the change in the MSA
unemployment rate. Specs without year effects include a quadratic in National GDP Growth. For IV estimates,
overidentification tests show we cannot reject the null of instrument validity. 5322 observations.
* p < 0.1, ** p < 0.05, *** p < 0.01.
OLS IV2 OLS IV2 IV2
Growth in real
housing price
0.181*** 0.384*** 0.092*** 0.285** 0.300**
(0.022) (0.127) (0.027) (0.132) (0.149)
F-test for Excl.
Instruments
45.3 47.1 41.4
MSA Effects Yes Yes Yes Yes Yes
Year Effects No No Yes Yes Yes
MSA Bartik Shock No No No No Yes
MSA Population Growth No No No No Yes

21. Extend Specification (from 1999 on) using Local “Small” Business Bank
Loan Supply Shocks (GMN (2015)
21

22. 22
ESTIMATES FOR 1999-2014
Sample:
IV estimates yield positive
and statistically significant
impact of housing prices and
small business loan supply
shocks on young-firm activity
shares.
Using small business activity
share as outcome yields
much weaker effects -- e.g.,
no effect of Small Business
Loan Supply shock.
OLS OLS IV2 IV2 IV2
Growth in real
housing price
0.178***
(0.022)
0.163***
(0.023)
0.297***
(0.090)
0.289***
(0.091)
0.322***
(0.102)
Local Small Business
Loan Supply Shock
0.030***
(0.010)
0.024**
(0.011)
0.020*
(0.012)
F-test for Excluded
Instruments
43.0 43.7 38.8
MSA Effects
Bartik Controls
Population Growth
Yes
No
No
Yes
No
No
Yes
No
No
Yes
No
No
Yes
Yes
Yes
Dependent Variable: Log Difference of Young Employment Share, Using MSA by Year Data
Notes: Standard errors in parentheses clustered at MSA level. All specs
include the Change in MSA level Unemployment Rate and a quadratic in
National GDP Growth. For IV estimates, overidentification tests show we
cannot reject the null of instrument validity. 3728 Observations.
* p < 0.1, ** p < 0.05, *** p < 0.01.

23. Local Employment Growth Rate Effects
23

24. Exploring the Channels Through
Which House Price Changes Affect Young-
Firm Activity Shares
24

25. (Local) Housing Prices and (Local)Young-Firm
Activity: Potential Transmission Channels
1. Wealth and Risk Tolerance: Home equity up  greater willingness to take on
risks of new/young business. (Khilstrom-Laffont, 1979, Guiso-Paiella, 2008)
2. Wealth Effect on Desire to Be Own Boss: Demand for being own boss is a
normal good and increases in wealth. (Hurst and Pugsley, 2015)
3. Liquidity and Collateral Effects: Households tap home equity to relax liquidity
constraints, increasing their ability to finance new/young businesses. Higher
house prices  greater collateral value. (Evans and Jovanovic, 1989)
4. Local Credit Supply: Local housing conditions affect local banks’ lending
capacity + young firms are relatively dependent on local bank credit.
5. Local Outlook and Credit Supply: Banks see local housing prices as indicators
of (future) local business conditions, affecting their willingness to lend; and
new and young firms are relatively dependent on bank credit.
6. Non-uniform Consumption Expenditure Responses: Young firms supply goods
and services whose demand is relatively sensitive to local income/wealth.25

26. Exploring Channels
Novel test to distinguish between contribution of consumption demand
channels and other channels (all of which operate on financial conditions for
the young firms and their owners)
Idea: Test whether the local industry growth rate response to local house price
changes depends on the local industry’s firm-age structure of employment. If
house prices work entirely through consumption demand channels, then we
expect the local industry response to be invariant to its firm-age structure.
The alternative view says the local industry response rises with its young-firm
activity share due to wealth, collateral, and liquidity effects of house prices on
the relative propensity to start a new business or expand a young business.
Implement using annual QWI employment data at 2-digit NAICS by MSA level
for 1999-2015.
26

27. Specifications for Implementing the Invariance Test
𝐺𝑅𝑗𝑚𝑡 = 𝑎 + 𝑏1 𝐶𝑌𝐶 𝑚𝑡 + 𝑏2 𝐻𝑃 𝑚𝑡 + 𝑏3 𝑒𝑚𝑝𝑠ℎ𝑎𝑟𝑒𝑗𝑚,𝑡−1
+𝑐 ∙ 𝐻𝑃 𝑚𝑡× 𝑒𝑚𝑝𝑠ℎ𝑎𝑟𝑒𝑗𝑚,𝑡−1 + 𝑓𝑡 + 𝑓𝑚 + 𝑓𝑗 + 𝜀 𝑗𝑚𝑡 (1)
𝐺𝑅𝑗𝑚𝑡 = 𝑎 + 𝑏3 𝑒𝑚𝑝𝑠ℎ𝑎𝑟𝑒𝑗𝑚,𝑡−1
+𝑐 ∙ 𝐻𝑃 𝑚𝑡× 𝑒𝑚𝑝𝑠ℎ𝑎𝑟𝑒𝑗𝑚,𝑡−1 +𝑓 𝑚𝑡 + 𝑓𝑗 + 𝜀 𝑗𝑚𝑡 (2)
𝐺𝑅𝑗𝑚𝑡 = 𝑎 + 𝑏3 𝑒𝑚𝑝𝑠ℎ𝑎𝑟𝑒𝑗𝑚,𝑡−1
+𝑐 ∙ 𝐻𝑃 𝑚𝑡× 𝑒𝑚𝑝𝑠ℎ𝑎𝑟𝑒𝑗𝑚,𝑡−1 +𝑓 𝑚𝑡 + 𝑓𝑗𝑡 + 𝜀 𝑗𝑚𝑡 (3)
where j is industry, m is MSA, and t is time. Industry classifications based
on 2-digit NAICS codes. 𝐺𝑅𝑗𝑚𝑡 is log employment change from t-1 to t for
industry j in MSA m, and 𝑒𝑚𝑝𝑠ℎ𝑎𝑟𝑒𝑗𝑚,𝑡−1 is the lagged young-firm share.27

28. Detail on Industry Classifications for This Test
23 Construction
31-33 Manufacturing
42 Wholesale Trade
44-45 Retail Trade
48-49 Transportation and Warehousing
51 Information
52 Finance and Insurance
53 Real Estate and Rental and Leasing
54 Professional, Scientific, and Technical Services
55 Management of Companies and Enterprises
56
Administrative and Support and Waste
Management and Remediation Services
62 Health Care and Social Assistance
71 Arts, Entertainment, and Recreation
72 Accommodation and Food Services
28
We omit the following industries because few MSAs have
positive employment and/or QWI coverage limitations:
11 -- Agricultural Services
21 -- Mining
22 -- Utilities
61 -- Educational Services (Mostly non-profits in QWI)
81 -- Other Services (Many religious organizations and other
non-profits in QWI).
The parts of 61 and 81 included in the QWI also have weak
relationships to cyclical variables, including housing prices.

29. Dependent Variable: Annual Log Employment Change at Industry-MSA level
OLS OLS OLS IV2
Chang in -0.939*** -0.750***
Unemp. Rate (0.149) (0.106)
Housing Price 0.088*** 0.175***
Log Chang (0.011) (0.042)
Young-Firm 0.029*** 0.031*** 0.037*** 0.031***
Emp. Share (0.010) (0.010) (0.010) (0.010)
HP x Young_Sh 0.813*** 0.780*** 0.588*** 0.672***
(0.059) (0.075) (0.091) (0.118)
MSA FE Yes No No Yes
Year FE Yes No No Yes
Industry FE Yes Yes No Yes
MSA-by-year FE No Yes Yes No
Ind-by-year FE No No Yes No
N 39627 39627 39627 39627
R2 0.140 0.267 0.317 0.137

30. Quantifying Departures from Age Invariance
• Are the departures from age invariance large in magnitude?
• To address this question, compute the regression-implied response differential
between an MSA-Industry at the 90 percentile for the young-firm employment
share and one at the 10 percentile. Evaluate at the local house price log change.
𝑅𝑒𝑠𝑝_𝐷𝑖𝑓𝑓 = Ƹ𝑐 𝑌𝑜𝑢𝑛𝑔_𝑠ℎ 𝑡
90−10
𝐻𝑃𝑡(𝑝), where
• Ƹ𝑐 = coefficient on interaction term in the regression. In practice, we use
Ƹ𝑐 = 0.672, the estimate from the rightmost column
• 𝑌𝑜𝑢𝑛𝑔_𝑠ℎ 𝑡
90−10
= the 90-10 differential in the young-firm employment across
local industries at time t
• 𝐻𝑃𝑡 𝑝 = pth percentile of log change from t-1 to t in MSA-level housing prices
• The lower panel on the next slide implements this calculation. We report the
annual average response differential during boom and bust periods and the
corresponding cumulative response differentials.
30

31. Dispersion in Young-Firm Employment Shares
Industry-MSA
Young-Firm Share
1999-
2015
Boom
Period
Bust
Period
90th Percentile 0.262 0.274 0.255
10th Percentile 0.049 0.063 0.048
Std Deviation 0.086 0.086 0.083
90-10 0.213 0.211 0.207
31
Dispersion in Local Log House Price Changes
Log MSA House
Price Change
1999-
2015
Boom
Period
Bust
Period
90th Percentile 0.078 0.128 0.035
10th Percentile -0.062 0.005 -0.138
Std Deviation 0.066 0.053 0.082
90-10 0.134 0.123 0.173
Use Ƹ𝑐 = 0.672 Boom Period Bust Period
P90 P10 P90 P10
𝐻𝑃𝑡 𝑝 (average annual log change) 0.128 0.005 0.035 -0.138
𝑌𝑜𝑢𝑛𝑔_𝑠ℎ 𝑡
90−10
0.211 0.207
𝑅𝑒𝑠𝑝_𝐷𝑖𝑓𝑓 Annual, Percentage Points 1.8 0.01 0.5 -1.9
Cumulative, Percentage Points 7.3 0.3 1.5 -5.8
Boom = 2002-2006 and Bust =2007-2010
These results show that departures from age invariance are large for SMSAs that had especially big house price
gains (losses) during the national house price boom (bust). However, the magnitude of departures from age
Invariance are modest for most SMSAs most of the time.

32. Additional Results from a Dynamic Specification
Extending the specification to include the lagged main effect for
local housing price changes and its interaction with the lagged
young-firm employment share in the local industry:
1. The local industry response to higher local house prices rises
even more steeply with the local industry’s young-firm share.
2. The effect of a local housing price increase on local industry
employment growth rises in period t with the local industry’s
young-firm share, and it rises even further in period t+1.
3. In terms of local industry employment levels, these results imply
powerful hysteresis effects of local housing price changes that
vary with the firm-age structure of employment in the local
industry. 32

33. How Important Are House Price
Movements and Bank Loan Supply
Shifts for National Changes in
Young-Firm Activity Shares?
33

34. Our Quantification Method
We use IV coefficient estimates and actual state-level house price
changes from 1981 to 2014 to quantify national effects of housing market
developments. By using all house price changes, we capture the effects of
exogenous house price changes and the role of house prices in transmitting
shocks that originate elsewhere. We aggregate state-level changes to the
national level using state-level employment shares.
Given correctly identified causal effects of local house price changes, our
quantification exercise may overstate or understate the role of housing
market developments in national young-firm activity shares:
• Overstate? Spatial equilibration of young-firm activity across local areas may
attenuate national responses relative to the aggregated local responses.
• Understate? (1) Positive spillovers of young-firm activity across local areas.
(2) Entrepreneurs may own houses outside the area where they operate
young firms, an effect not captured by our regression model or aggregation.
34

35. 35
Contribution of Housing Price Changes to Log Changes in Young-Firm Employment
Shares By Cycle Episode Based on IV2 Estimate of Coefficient on HP
Solid Bar is Actual. Striped and Dotted Bars are counterfactuals implied by IV (2) approach with, respectively,
controls for MSA, Year Effects and Local controls. Counterfactuals use actual state-level house price changes.
Annualized deviations from overall means depicted. The mean decline is -2.2 log points per year.
61 percent of the
(trend-deviated) decline
in young-firm activity share
in Great Recession is due
to decline in housing
prices, according to
this exercise.
-5
-4
-3
-2
-1
0
1
2
3
LogPoints

36. 36
Year-By-Year results show that the housing boom attenuated the secular decline in young-firm
employment shares from from 1998-2007 and accelerated the decline after 2007. IV(2) estimates.
Recall that the
mean change
in the young firm
employment share
is -2.2 log points
per year.
-10
-8
-6
-4
-2
0
2
4
6
8
10
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
LogPoints
Actual Housing Prices (IV Results)
Cumulative increase from 1997-2007 from
Housing Prices = 11 log points
Cumulative decrease from 2008-2013
From Housing Prices = 10 log points

37. 37
Contribution of Housing Price Changes and “Small” Business Bank Loan Supply
Shocks to Log Changes in Young-Firm Employment Share by Cycle Episode
Sold Bar is Actual, Diagonal Striped Bar is Counterfactual (Housing Prices only), Dotted Bar is Counterfactual (Loan Supply only), Horizontal
Striped Bar is (Housing Prices + Loan Supply). Using IV2 estimates from column 4 of previous table. Annualized deviations from overall
means depicted. The mean decline is 2.4 log points per year from 1999-2014.
Taken together, the decline
in housing prices and bank
loan supply shocks
account for 85 percent of
trend-deviated decline in
Young-Firm activity share
in Great Recession
During 2001-08 period these
effects tended to boost
Young-firm activity shares –
working against forces leading
to diminished dynamism over
this period of time.
-4
-3
-2
-1
0
1
2
3
LogPoints

38. How Do the Fortunes of Young Firms
Affect the Labor Market Opportunities of
Younger and Less-Educated Workers?
38

39. 39

40. 40
1. Concentration of
secular decline in Great
Recession is apparent
for all groups.
2. Younger workers and
less educated workers
have larger declines
than national average
in Great Recession
(2008-10). In turn,
older workers and
more educated
workers have smaller
declines in Great
Recession than
national average.

41. 41
Regressions at the MSA Level
Regressions at the
MSA-Industry Level
Men Women Men Women
Demographic
Group OLS (1)
Two-Stage
(2) OLS (3)
Two-Stage
(4)
Two-Stage
(5)
Two-Stage
(6)
19-24 Years of
Age
0.017
(0.010)
0.258
(0.046)
0.01
(0.009)
0.239
(0.043)
0.259
(0.052)
0.384
(0.062)
25-44
0.028
(0.010)
0.359
(0.074)
0.032
(0.009)
0.550
(0.108)
0.360
(0.076)
0.633
(0.119)
45-54
-0.028
(0.011)
-0.540
(0.093)
-0.031
(0.008)
-0.588
(0.099)
-0.539
(0.096)
-0.786
(0.134)
55-64
-0.017
(0.005)
-0.082
(0.027)
-0.012
(0.005)
-0.201
(0.036)
-0.077
(0.025)
-0.230
(0.041)
< High School
0.020
(0.007)
0.283
(0.053)
0.021
(0.007)
0.295
(0.053)
0.289
(0.048)
0.313
(0.051)
High School
0.01
(0.007)
0.110
(0.024)
0.016
(0.007)
0.151
(0.027)
0.062
(0.019)
0.089
(0.023)
Some College
-0.010
(0.002)
-0.105
(0.021)
-0.017
(0.005)
-0.195
(0.031)
-0.126
(0.024)
-0.166
(0.026)
Undergrad or
More
-0.022
(0.010)
-0.294
(0.055)
-0.020
(0.009)
-0.257
(0.051)
-0.225
(0.042)
-0.238
(0.045)
Dependent variable: One-Year Change in the group-level share of employment at the MSA
Level or the MSA-Industry Level
Reported coefficients
are effect of change in young
firm employment share.
Two-stage approach isolates
systematic variation in young firm
employment share projected
from first stage on local
Cyclical shocks, housing price
Growth and small business
Lending shocks. This
addresses measurement
error from noise infusion
for the QWI.
All specifications include MSA
Effects and aggregate cyclical
Controls. Industry specs control
For industry effects and industry
Interacted with national cyclical controls

42. Summary of Main Results
1. Over the past 35 years, there has been a large secular decline
in the U.S. young-firm employment share. That decline
accelerates during recessions and attenuates during booms.
2. Local house price changes have large causal effects on the local
employment shares of young firms.
3. These causal effects work partly through wealth, liquidity,
collateral, and credit supply effects on the propensity to start a
new business or expand a young one.
4. House price changes are a major driver of medium-run
fluctuations in national young-firm employment shares. They
account for more than half of the trend-deviated drop in
young-firm activity shares during the Great Recession. 42

43. Summary of Main Results
5. Locally exogenous shifts in bank loan supply are also a driver
of local and national changes in the young-firm employment
share in certain episodes, e.g., the Great Recession.
6. When the young-firm share of local employment rises,
employment shifts from older to younger workers and from
more- to less-educated ones.
7. Together with our other results, Result 6 says that housing
busts and credit crunches hurt younger and less-educated
workers through their particular effects on the fortunes of
younger firms in addition to their broader effects on the
economy.
43