Transcript Lecture 5: More on Labor Supply

Lecture 6:
Entreprenuership
Part 1:
Some Data Sources For Entrepreneurship
Data for Today’s Class
•
Distribution of Firms by Size and Age (aggregates)
Statistics of U.S. Businesses (SUSB)
http://www.sba.gov/advo/research/data.html
Measures number of employees, number of firms, births, deaths, and
growth (all in total and by firm size) by detailed industries.
Allows one to compute the distribution of firm size over time by industry.
Large literature trying to explain the size distribution of firms.
Data for Today’s Class
•
Micro Data on Small Firms
Survey of Small Business Finances (SSBF)
http://www.federalreserve.gov/ssbf/
Conducted by Board of Governors
1987, 1993, 1998, and 2003 (repeated cross sections)
Measures detailed descriptive and financial data on firms with less than 500
employees (non-agricultural)
Data for Today’s Class
•
Micro Data on Small Firms
Kauffman Firm Survey (KFS)
http://www.kauffman.org/research-and-policy/kauffman-firm-survey.aspx
Conducted by Kauffman Foundation
2004 – 2007 (Longitudinal Survey)
Measures detailed descriptive and financial data on small businesses
Starts with new businesses in 2004.
Follows survivors through 2007.
Data for Today’s Class
•
Micro Data on Small Firms
PSID (longitudinal)/CPS (some longitudinal/mostly repeated cross sections)
Collects data on the self employed.
Collects data on business owners.
Most micro data empirical work on “entrepreneurs” use household level
data where entrepreneur is equated with the self employed or with small
business owners.
Cool Data that is Underutilized
•
Longitudinal Business Database
From U.S. Census (available for use only at Census Research Data Centers
(there is one at the Chicago Fed).
Tracks the dynamics of all firms in the U.S. (across all industries and all
sizes) since 1976 (including births and deaths).
Based on tax records.
Part 2:
Standard Model: Liquidity Constraints and
Small Business Formation
Why Do People Start Businesses?
•
Small Business Skills (Innovators) (Schumpter (1934), Evans and
Jovanovic (1989))
•
Risk Preferences (Kihlstorm and Laffont (1979), Jovanovic (1979))
•
“Jack of All Trades” (have better management skills) (Lazear (2005))
Two major questions in the literature:
Why can’t innovation take place in the existing firms?
Can the new firms get financing?
Evans and Jovanovic (1989)
Choice:
Become a worker:
Earn wage:
(wζ)
Become an “entrepreneur”:
Earn income:
( y   k )
where: θ is entrepreneurial ability (know when making choice)
k is capital necessary to start a business
ζ is returns to scale on capital:   (0,1)
Note:
Assume innovations to w and y are uncorrelated.
Assume that ability (θ) is uncorrelated with market wage.
Assume risk neutrality.
Evans and Jovanovic (1989)
Entrepreneurial Income:
y  r(z  k )
where: z is initial wealth
Constraint:
0  k  z
(where   1)
Firms can at most borrow λ times their initial wealth to fund their capital
project.
Note:
Borrowing rate = lending rate = r (same for everyone).
Optimal Capital Stock
max [ k   r ( z  k )]
k[0,  z ]
F .O.C. :
 k  1  r  0
1/(1 )
  
k 

 r 
Implication, entrepreneur is unconstrained when:
  ( z)
1
r

Probability of Entrepreneurship Increasing in Wealth
Finish Solving The Model
Entrepreneurial Income as a function of constrained/unconstrained k.
Compare Entrepreneurial Earnings to Wages
max[ k   r ( z  k )]  w  rz
Unconstrained:

w(1   )
 1
r 
1



(

z
)
 
 
r 
 
 
Constrained:

  max ( z )1

r




1 
 , w( z )  r ( z ) 


Evans and Jovanovic Conclusions
• Richer households are less bound by liquidity constraints and as a result
are more likely to enter entrepreneurship.
• Should see a positive relationship between initial wealth and entry into
small business ownership.
Part 3:
Testing for the Importance of Liquidity
Constraints
Old School Tests of Liquidity Constraints for Entrepreneurs
Basically, the majority of empirical papers regress business ownership (the
propensity to become a business owner, the propensity to survive as a business
owner) on household wealth.
Prob (Start Business (t, t+1)) = α0 + α1 ln(Wealth(t)) + γ X + ε
Early research concluded that if wealth is significant in predicting business entry,
liquidity constraints are binding. (i.e., α1 > 0)
Approach taken:
Evans and Jovanovic (1989, JPE)
Evans and Leighton (1989, AER)
Fairlie (1999, Journal of Labor Economics)
Quadrini (1999, Review of Income and Wealth)
Limitations of Approach
Is the level of wealth exogenous from other factors that cause entrepreneurial entry?
High ability earn more (accumulate more for retirement) and may be better at
innovating.
Risk preferences can cause high wealth and taste for entrepreneurship
People planning for self employment accumulate assets for their retirement (do not
have pensions).
Try to find an “instrument”.
Inheritances as an Instrument
•
Instrument for wealth - look at liquidity windfalls which are
uncorrelated with the decision to become an entrepreneur.
•
Many use inheritances as instrument.
•
Find inheritances are strongly correlated with entrepreneurial entry.
Receiving an inheritance in year t predicts entrepreneurial entry between t
and t+k.
•
Holz-Eakin, Joulfaian, and Rosen (JPE, 1994)
•
Blanchflower and Oswald (1998, Journal of Labor Economics).
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Up Though 2003: Conventional Wisdom
•
Liquidity constraints are an important deterrent to small business
formation.
•
Liquidity constraints to small business formation is an important
explanation of the dispersion in wealth (rich people keep accumulating
wealth to relax their liquidity constraint for their small business).
- Cagetti and DeNardi (2006, JPE).
•
Welfare costs of liquidity constraints to entrepreneurship is large
- Buera (2009, Annals of Finance)
Both papers use as the basis of their models, the relationship between wealth
and starting a business using household micro data.
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A Re-Evaluation of The Facts
Liquidity Constraints, Household Wealth and
Entrepreneurship?
Erik Hurst
University of Chicago and NBER
Annamaria Lusardi
Dartmouth College and NBER
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Goal
• Are people interpreting the data correctly?
This paper
• I think that the relationship between wealth and small business start-up using
micro data (or firm level data) is not what people think.
Paper with Ben (coming later)
•
In the micro data, do small business match our conceptual models of “entrepreneurs”?
•
If not, what can explain the propensity to become small business owners in the data?
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Some Facts About Small Business Owners
• How much money do small business owners need to start their business?
• 1987 NSSBF: Median amount of capital to start a business is $22,700
25% start with less than $5,000
• 1982 Characteristics of Business Owners (Meyer 1990) report even smaller
figures:
– 63% of non minority males and 78% of black business owners started with less
than $8,700 (1996 dollars)
• Inc Magazine 500 fastest growing companies in the U.S. (Bhidé 2000)
– 26% started with less than $5,000 in upfront capital
– Median was not much higher.
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Starting Capital Value
1st Quartile
Median
3rd Quartile
% of Firms
Construction
$2,860
$9,500
$30,100
10.9%
Services
$3,450
$19,400
$62,719
30.3%
Mining
$1,730
$37,800
$394,375
1.2%
Transportation,
Communication and
Public Utilities
$15,120
$47,300
$143,300
3.0%
Finance, Insurance and
Real Estate
$7,900
$36,500
$173,260
4.8%
Manufacturing
$16,165
$47,300
$151,200
7.9%
Wholesale Trade
$11,010
$41,400
$145,860
8.5%
Retail Trade
$21,880
$55,200
$118,150
33.3%
Industry
Low Starting Capital
Industries
High Starting Capital
Industries
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What We Do in this Paper
•
Formally Test The Importance of Liquidity Constraints and Business
Ownership
–
–
–
Examine the relationship between own wealth and business entry
Examine the relationship between parental wealth and business entry
Look at the wealth/business entry relationship by types of business
–
Instruments for wealth changes
•
•
–
Inheritances
Capital gains on housing.
Look at survival probabilities
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Data Source
•
•
Panel Study of Income Dynamics (PSID)
Can follow households in and out of business ownership. Business ownership is
asked in every year. Business wealth (and all other wealth) asked every five years
starting in 1984.
•
Main sample of analysis focuses:
Stacked panel: Transition into business ownership between 1989 and 1990 and
Transition into business ownership between 1994 and 1995
Focus on:
Non business owners
Households aged 22 to 60
Sample size:
7,645 observations (almost 5,000 distinct households).
For some analysis, we will only use the 1989-1990 panel (occupation and industry
codes are not available beyond 1993). 3,645 observations.
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Initial Methodology
• Run three different types of regressions
Prob (Start Business (t, t+1)) = α0 + α1 Wealth(t) + γ X + ε
Prob (Start Business (t, t+1)) = α0 + α1 Wealth(t) + α2 Wealth(t)2 +
α3 Wealth(t)3 + α4 Wealth(t)4 +
α5 lnWealth(t)5 + γ X + ε
Prob (Start Business (t, t+1)) = α0 + α1 Dummy_Wealth_80-95 +
α2 Dummy_Wealth_95+ γ X + ε
• X includes controls for age, education, income, family structure, prior
employment status, and prior business ownership.
• Wealth is defined as the sum of savings and checking accounts, bonds,
stocks, IRAs, housing equity, other real estate, and vehicles, minus all debts.
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0-80th
Percentile
80th - 95th Percentile
95th-98th Percentile
Predicted Probability
7.0%
6.0%
5.0%
4.0%
3.0%
2.0%
-2
0
20
60
0
10
0
14
0
18
0
22
0
26
0
30
0
34
0
38
0
42
0
46
0
50
Wealth Level (in $1,000)
29
30
Importance of Parental Wealth
Variables
Include a full set of income and demographic
controls?
Yes
Household’s Own Non-Business Net Worth in 1989
8.78 E-8 (6.98 E-8)
Dummy: Husband/Wife Father a Business Owner?
0.049 (0.023)
Dummy: Parental Wealth 20th - 40th percentile
0.024 (0.020)
Dummy: Parental Wealth 40th - 60th percentile
0.002 (0.018)
Dummy: Parental Wealth 60th - 80th percentile
0.021 (0.019)
Dummy: Parental Wealth 80th - 90th percentile
0.032 (0.021)
Dummy: Parental Wealth 90th - 97th percentile
0.025 (0.024)
Dummy: Parental Wealth > 97th percentile
0.072 (0.039)
31
Wealth and Business Start Up by Industry
• Wealth should be more important for starting a business with high starting
capital requirements.
• You need to be rich to start a car factory. However, wealth should not matter
much to start a house-cleaning business.
We explore heterogeneity in starting businesses of differing starting capital
amounts. Perhaps the heterogeneity is masking evidence that liquidity
constraints exist.
Create Two Categories:
1. Low Starting Capital (Construction and Services)
2. High Starting Capital (FIRE, Manufacturing, Transportation, Wholesale and
Retail Trade, Communications)
Note:
PSID has two additional industries: Farming and Professionals
We will look at professionals separately
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4.0%
0-90th Percentile
90th - 98th Percentile
Predicted Probability
3.0%
2.0%
High Starting
Capital Industries
1.0%
Professional
Industries
Low Starting Capital Industries
0.0%
0
40
80
0
12
0
16
0
20
0
24
0
28
0
32
0
36
0
40
0
44
0
48
Wealth Level (in $1,000)
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What about Inheritances as an Instrument?
•
Fact is replicated in our data set.
1.
Many business are transferred at the time of death (5% of NSSBF sample)
2.
More importantly, inheritances are not randomly distributed in the
population.
Is the case closed? No…… Why?
Those who get inheritances are just different (on average) from those who
do not.
A counterfactual……
Test of the latter proposition 
Do future inheritances (received after the business is started) predict
current business entry?
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35
A New Instrument
We use an alternative measure of liquidity: Regional variation in housing prices.
Much evidence that households do borrow against home equity to sustain
consumption or finance investment projects.
– Brady, Canner and Maki (2000) – 20% of those who removed equity during the
late 1990s when refinancing used it to fund business investment.
– Hurst and Stafford (2002) – find household who lost their jobs in the early 1990s
used home equity to prop up consumption.
We predict that households who receive increases in home equity – all else equal –
should have access to more liquidity.
Are they more likely to start a business? We find NO effect of housing capital gains
on business entry!
36
Some Additional Facts about New Business Owners
37
Conclusions For Policy Crowds…
• Our findings do NOT promote cutting funding to the Small Business
Administration (SBA). Part of the reason why liquidity constraints may not
be binding is because of SBA policies.
• Existing evidence on the existence of liquidity constraints for small
businesses not very conclusive.
• Why is it the effect is so large for the really rich?
Outstanding Questions:
• Are the business owners in typical household or business survey important
for economic growth?
• Are there existing households who would start a profitable business if they
had wealth that just are not showing up in the data?
• What drives business ownership decisions for median household?
38
Part 5:
The Non-Pecuniary Benefits of “Entrepreneurship”
Some Interesting Facts…
• Does the data on small business owners match the concept of
entrepreneurship in our model?
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Industry
Table 1: Data on Employer Firms from Statistics of U.S. Businesses (SUSB): 1989 – 1997 Pooled
Percent of
Percent of
Within
Share
Firms with Firms with
Within
Industry
Births of
1-9
1-99
Industry
Percent
Firms with
Percent
Percent
Employees Employees
Percent
Employees
1-9
Firms of
Employees
Out of All
Out of All
Firms
In Firms
Employees
Out of all
Out of All Firms With Firms With
With 1-9
with 1-9
Out of All
Firms
Employees
1-9 Emps.
1-99 Emps Employees Employees
Births
All Firms
Fraction Of
Firms More
than 10
Years Old
in Industry
With 1-9
Employees
-
-
-
-
0.606
0.120
0.735
Retail Trade
0.180
0.139
0.153
0.155
0.473
0.154
0.101
0.780
General Services
0.162
0.114
0.193
0.179
0.746
0.295
0.137
0.851
Health Care Services
0.100
0.126
0.109
0.106
0.589
0.110
0.051
0.806
FIRE
0.096
0.073
0.091
0.083
0.473
0.144
0.063
0.860
Construction
0.091
0.051
0.117
0.108
0.727
0.236
0.104
0.784
Wholesale Trade
0.081
0.066
0.070
0.079
0.526
0.133
0.049
0.638
Accomm./Food Service
0.077
0.088
0.056
0.071
0.472
0.074
0.048
0.581
Professional Services
0.069
0.058
0.085
0.078
0.675
0.182
0.063
0.827
Manufacturing
0.063
0.193
0.046
0.060
0.370
0.033
0.033
0.598
Transportation/Warehouse
0.042
0.059
0.035
0.036
0.403
0.080
0.033
0.695
Entertainment Services
0.019
0.018
0.019
0.020
0.608
0.121
0.017
0.755
Agriculture/Mining
0.018
0.013
0.022
0.021
0.566
0.137
0.018
0.799
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Small Business Employment Share vs. Probability of Growth
.35
By 2 Digit Industry
.2
.25
.3
30
26
16
34
36
33
3235 14
2220
25
2848 23
24
3738
29
83
82
3958
84
45
52
44
79
61
31 70
46 12
50
27
73
54
42
55
41
8
80
21
51
78
49
53
87
56
62
60
10
63
13
7
17
15
47
57
59
75
86
76
72
89
9
81
64
65
.15
67
0
.1
.2
.3
Share of Industry Employment in Small Firms
Fitted values
.4
grow_1_9_timeavg
42
Industry
Table 2: Statistics from the 2004-2008 Panel of New Firms from the Kaufman Firm Survey
I
II
III
IV
Percent w/ Percent w/
Percent
Percent
Percent
Percent
ΔEmps.
ΔEmps.
Percent
With
Percent of
with 0-4
with 0-9
with 20+
>5
> 10
With
TradeFirms
Employees Employees Employees Over 4 yrs Over 4 yrs
Patent
mark
All
Percent With
Patent or
Trade-mark
81.7%
91.6%
3.5%
10.8%
3.6%
2.7%
8.9%
10.2%
Profess. Services
19.4
86.0
96.4
1.1
8.2
1.7
3.5
14.6
15.5
General Services
18.3
86.8
92.8
3.1
9.8
4.3
1.9
7.3
8.2
Retail Trade
13.9
84.1
94.0
1.7
8.2
0.8
0.8
7.5
7.2
Construction
11.7
77.1
88.5
6.5
16.9
8.0
0.4
1.7
2.0
FIRE
10.5
88.9
95.5
2.9
5.4
1.0
0.5
4.1
4.1
Manufacturing
6.2
79.0
88.8
6.1
12.3
6.1
16.0
8.5
19.2
Wholesale Trade
4.9
75.0
87.8
4.6
16.9
3.7
6.8
8.8
13.5
Health Services
3.2
71.2
88.6
4.9
14.6
5.9
0
7.7
7.7
Transportation
3.0
80.1
90.4
2.3
14.6
3.8
0
1.6
1.6
Information
3.0
74.9
89.4
1.5
10.9
1.4
1.2
34.8
35.4
Arts/Ent. Services
2.7
81.8
97.6
0
0
0
0
20.5
20.5
Restaurant/Hotel
2.0
28.0
40.3
20.4
33.7
15.1
0
10.0
10.0
Ag/Mining
1.6
70.4
89.8
10.2
15.7
6.9
10.3
4.5
14.8
43
Industry
Table 3: Data on the Self Employed from the Current Population Survey (CPS): 1992-2002
Fraction of
Fraction of
Continuing Self
Average Year over
Continuing Self
Employed Firms
Percent of Self
Year Wage Change
Employed Firms
That Increased
Employed in Each
For Entrants Into
That Increased
Workers in the Firm
Industry
Self Employment
Workers in the Firm By Five Employees
Net Change in
Employees by
Continuing Self
Employed Firms By
Industry
Retail Trade
0.105
-0.063
0.055
0.015
-0.005
General Services
0.175
-0.128
0.029
0.002
0.004
Professional Services
0.051
-0.022
0.049
0.004
-0.002
Health Care Services
0.069
-0.074
0.050
0.006
-0.074
FIRE
0.235
-0.030
0.040
0.000
-0.003
Construction
0.053
-0.109
0.106
0.013
0.028
Accomm./Food Service
0.033
-0.006
0.097
0.007
-0.013
Wholesale Trade
0.079
-0.029
0.044
0.002
-0.021
Manufacturing
0.060
-0.048
0.084
0.003
0.002
Transportation/Warehouse
0.057
-0.023
0.049
0.005
-0.012
Entertainment Services
0.026
-0.029
0.043
0.005
0.010
Agriculture/Mining
0.047
-0.307
0.033
0.000
0.009
44
Two Additional Results of
• Moskowitz and Vissing Jorgensen (AER 2002)
“Private Equity Puzzle”….Measured risk adjusted return to public equity is
much higher than the measured risk adjusted return to private equity.
• Hamilton (JPE 2000)
Wages of individuals fall sharply (~30% at median) when household
transition into small business ownership from wage workers.
Potential explanation:
There are non-pecuniary benefits to small business
formation.
Consistent with micro data that most small firms never grow, never innovation
and are concentrated in a few industries.
45