Transcript Lecture 5: More on Labor Supply

Topic 8:
Understanding Small Businesses
An Overview
1.
To What Extent Do the Self Employed Lie About Their Earnings To
Household Surveys ?
o
2.
Subtitle:
Can we trust the income data in household surveys?
How much do ex-ante differences in motivations to start a small business
explain differences in ex-post small business outcomes?
o
Subtitle:
Is it correct to equate small business owners with
“entrepreneurs”?
3.
How Important are Liquidity Constraints to Small Businesses?
4.
How Important are Non-Pecuniary Benefits In Explaining the Behavior of
Small Businesses?
o
At a minimum, how should one think about small business dynamics in a
world where non-pecuniary benefits are important?
Part A:
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
α 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:

 1  r 
w(1   )      ( z )1
 
r 
 
 
Constrained:

1  r
  max ( z ) 




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 B:
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?
o High ability earn more (accumulate more for retirement) and may be
better at innovating.
o Risk preferences can cause high wealth and taste for entrepreneurship
o People planning for self employment accumulate assets for their
retirement (do not have pensions).
•
Next Generation of Studies:
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.
o Many use inheritances as instrument.
o Find inheritances are strongly correlated with entrepreneurial entry.
o 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).
15
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).
o Cagetti and DeNardi (2006, JPE).
•
Welfare costs of liquidity constraints to entrepreneurship is large
o Buera (2009, Annals of Finance)
Note:
Both papers use as the basis of their models, the relationship
wealth and starting a business using household micro data.
between
16
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
17
Goal
• Are people interpreting the data correctly?
This paper
• We think that the relationship between wealth and small business start-up
using micro data (or firm level data) is not what people think.
18
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.
19
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
20
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
21
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.
22
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.
23
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)
24
25
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)
26
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
27
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)
28
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?
29
30
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!
31
Some Additional Facts about New Business Owners
32
Conclusions (part B)
• 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?
33
Part C:
“What Do Small Businesses Do?”
(with Ben Pugsley)
Some Background
•
There is a disconnect among researchers and policy makers between the
theoretical/conceptual models of “entrepreneurs” and the “universe of small
business owners” on which we test theory/implement policy.
o
“Bill Gates” types (conjecture: they are rare)
Match the theoretical concept
Innovate, efficiently share risk, has new idea/product, wants to grow,
innovation has social spillovers, etc.
o
“Joe the Plumber” types (conjecture: they are not rare)
My brother
Does not want to grow, does not want to innovate, very content
staying small, does not innovate ex-post, etc..
Some Outstanding Questions
•
Question:
What drives the decision to become a small business owner
for the “Joe the Plumber” types?
•
Question:
How do these other small businesses respond to the incentives
we provide to stimulate entrepreneurial activity for the “Bill
Gate” types?
•
Question:
Do individuals really want to innovate and/grow when they
start their business?
Data Sources
To answer these questions, we are going to use a variety of data sources from:
•
Statistics of U.S. Businesses (SUSB) – maintained by Census using data from
U.S. Business Register.
o
•
Focuses on employer firms (excludes non-employers)
National Survey of Small Business Finances
o
Focuses on firms with between 1 and 500 employees
•
Kauffman Firm Survey – Survey of new businesses; has panel dimension
•
Panel Study of Entrepreneurial Dynamics – Survey of new businesses; has
panel dimension.
Some Background Facts
•
~ 6 million employer firms in the U.S. in 2007
•
Aside:
•
About 90% of employer firms have less than 20 employees.
•
About 20 percent of employment in is firms with less than 20 employees.
~ 22 million non-employers (which comprise about 4% of
payrolls)
Some Background Facts
1
0.9
0.8
Cummulative Share
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Firm Size
Firms
Establishments
Employment
Payroll
Receipts
Who Are The Small Business Owners?
•
Define small business owners as those with less than 20 employees (or 100
employees).
•
Use data from the 2003-2007 Statistics of U.S. Businesses (SUSB) – compiled
by the U.S. Census.
•
Group all small businesses (across all industries) into 4-digit NAICS industries
(there are about 300 4-digit NAICS codes).
•
Define:
xj 
sj
s
j
j
•
xj is share of small business in industry j out of all small businesses.
Small Businesses (< 20 Emps) by 4 digit Industry
Top 15
Full-Service Restaurants (7221)
Offices of Physicians (6211)
Limited Service Restaurants (7222)
Religious Organizations (8131)
Build. Equip Contractors (2382)
Dentists (6212)
Auto Repair (8111)
Legal Services (5411)
Res. Bldg Construction (2361)
Service to Build. (5617)
Build. Finishing Contractors (2383)
Build. Exterior Contractors (2381)
Insurance Agents (5242)
Other Health Practitioners (6213)
Arch./Engineering Services (5413)
Sum of Top 15
xj (%)
4.0
3.7
3.5
3.5
3.3
3.1
2.6
2.6
2.5
2.5
1.9
1.9
1.8
1.7
1.6
~40%
15-30
Accounting Services (5412)
Personal Care Services (8121)
Consulting Services (5416)
Gas Stations (4471)
Child Day Care Services (6244)
Lessors of Real Estate (5311)
Other Professional Serv. (5419)
Computer System Design (5415)
Other Specialty Contractors (2389)
Business/Political Orgs (8139)
Grocery Stores (4451)
Other Recreation Industries (7139)
Building Material Dealers (4441)
Pharmacies (4461)
Real Estate Agents/Brokers (5312)
Summary of Top 30
Note: This patterns hold adjusting for industry size.
xj (%)
1.6
1.5
1.4
1.3
1.3
1.2
1.2
1.2
1.1
1.1
1.1
1.0
1.0
0.9
0.9
~60%
Small Businesses (< 20 Emps) by 4 digit Industry
Top 15
Full-Service Restaurants (7221)
Offices of Physicians (6211)
Limited Service Restaurants (7222)
Religious Organizations (8131)
Build. Equip Contractors (2382)
Dentists (6212)
Auto Repair (8111)
Legal Services (5411)
Res. Bldg Construction (2361)
Service to Build. (5617)
Build. Finishing Contractors (2383)
Build. Exterior Contractors (2381)
Insurance Agents (5242)
Other Health Practitioners (6213)
Arch./Engineering Services (5413)
Sum of Top 15
xj (%)
4.0
3.7
3.5
3.5
3.3
3.1
2.6
2.6
2.5
2.5
1.9
1.9
1.8
1.7
1.6
~40%
15-30
Accounting Services (5412)
Personal Care Services (8121)
Consulting Services (5416)
Gas Stations (4471)
Child Day Care Services (6244)
Lessors of Real Estate (5311)
Other Professional Serv. (5419)
Computer System Design (5415)
Other Specialty Contractors (2389)
Business/Political Orgs (8139)
Grocery Stores (4451)
Other Recreation Industries (7139)
Building Material Dealers (4441)
Pharmacies (4461)
Real Estate Agents/Brokers (5312)
Summary of Top 30
Note: This patterns hold adjusting for industry size.
xj (%)
1.6
1.5
1.4
1.3
1.3
1.2
1.2
1.2
1.1
1.1
1.1
1.0
1.0
0.9
0.9
~60%
Small Businesses (< 20 Emps) by 4 digit Industry
Top 15
Full-Service Restaurants (7221)
Offices of Physicians (6211)
Limited Service Restaurants (7222)
Religious Organizations (8131)
Build. Equip Contractors (2382)
Dentists (6212)
Auto Repair (8111)
Legal Services (5411)
Res. Bldg Construction (2361)
Service to Build. (5617)
Build. Finishing Contractors (2383)
Build. Exterior Contractors (2381)
Insurance Agents (5242)
Other Health Practitioners (6213)
Arch./Engineering Services (5413)
Sum of Top 15
xj (%)
4.0
3.7
3.5
3.5
3.3
3.1
2.6
2.6
2.5
2.5
1.9
1.9
1.8
1.7
1.6
~40%
15-30
Accounting Services (5412)
Personal Care Services (8121)
Consulting Services (5416)
Gas Stations (4471)
Child Day Care Services (6244)
Lessors of Real Estate (5311)
Other Professional Serv. (5419)
Computer System Design (5415)
Other Specialty Contractors (2389)
Business/Political Orgs (8139)
Grocery Stores (4451)
Other Recreation Industries (7139)
Building Material Dealers (4441)
Pharmacies (4461)
Real Estate Agents/Brokers (5312)
Summary of Top 30
Note: This patterns hold adjusting for industry size.
xj (%)
1.6
1.5
1.4
1.3
1.3
1.2
1.2
1.2
1.1
1.1
1.1
1.0
1.0
0.9
0.9
~60%
0
.1
.2
.3
.4
xj vs. within industry share of small firms
1
2
3
4
5
6
7
8
9
10
Decile of Unadjusted Industry Firm Share out of all Small Firms
Heterogeneity in Ex-Post Small Business Outcomes
•
Most small businesses do not grow
•
Most small businesses do not innovate
Traditional Explanations
-
Differences in Luck
-
Differences in Ability
-
Differences in Constraints (ability to borrow/self finance)
Most Small Businesses Do Not Grow (Stay Small)
Firm Age
Industry
0-10 Years Old
10-25 Years Old
All Firm Ages
All
92.0
85.7
87.2
FIRE
95.5
91.8
91.9
Agriculture
94.8
88.1
91.6
Construction
93.7
86.0
88.9
Wholesale Trade
93.0
83.2
84.1
Services
92.7
88.4
89.1
TCU
92.3
82.2
86.0
Retail
88.6
81.8
84.6
Manufacturing
85.5
71.5
72.4
Most Small Businesses Do Not Grow (Stay Small)
Firm Age (Measuring Employment Share)
Industry
0-10 Years Old
10-25 Years Old
All Firm Ages
All
44.8
24.7
19.4
FIRE
50.8
31.7
19.0
Agriculture
57.7
47.1
50.1
Construction
59.1
38.4
39.4
Wholesale Trade
52.8
30.6
21.7
Services
40.7
23.1
20.8
TCU
44.2
14.7
11.8
Retail
46.9
24.8
18.8
Manufacturing
34.6
16.0
8.5
Survey of Small Business Finances: Business Growth
Percent Changing Employment Over Last 3 Years
Age of Firm
Direction of Emp. Change
1-10 Years
11-20 Years
20+ Years
All
Increase Employment
27.6
19.4
15.3
21.3
No Change in Employment
61.0
65.0.7
72.5
65.6
Decrease Employment
11.3
15.7
12.2
13.1
Sample Size
847
818
725
2,386
Kauffman Firm Survey (KFS): Business Growth
Percent Changing Employment Over Last 4 Years
Percent With
Percent With
Percent With
Δ Employment > 1
Δ Employment > 5
Δ Employment > 10
Employee
Employees
Employees
All New Firms
41.9
10.8
3.6
Sample Size
2,617
2,617
2,617
Survey tracks new firms (on eve of establishment): Focus on survivors.
Small Business Gross Job Creation
•
Most small firm gross job creation does NOT occur in the industries
which are dominated by small businesses.
•
To the extent small businesses create jobs, it is not in the skilled craftsmen,
skilled professional, and small shopkeeper sectors.
•
Google created a lot of jobs.
•
Google doesn’t look like the typical small business.
New Data: Panel Study of Entrepreneurial Dynamics
•
Samples ~1200 Nascent “Entrepreneurs” (from an initial sample of
35,000)
•
Initial wave was late 2005.
•
Individuals were on the cusp/in the process of starting a business
•
Has a panel dimension
•
Initial wave asked respondents about their expectations/desires for their
business
Most Small Businesses Do Not Innovate
Data From the Panel Study of Entrepreneurial Dynamics
Sample 1:
All
Sample 2:
Positive
Revenues In
Year 2006
Sample 3:
Positive
Revenues in
Year 2010
Percent of Firms that Already Developed
Proprietary Technology, Processes, or Procedures
6.5
8.3
1.1
Percent of Firms that Already Applied for Patent,
Copyright, or Trademark
4.9
6.0
3.3
Percent of Firms Stating That Many Existing Firms
Already Offer Same Product/Service to Expected
Customer Base
35.7
43.3
39.6
Percent of Firms Stating That No Existing Firms
Offers Same Product/Service to Expected
Customers
19.2
13.3
17.3
Sample Size
1,214
602
162
Most Small Businesses Do Not Innovate
Data From the Panel Study of Entrepreneurial Dynamics
Sample 1:
All
Sample 2:
Positive
Revenues In
Year 2006
Sample 3:
Positive
Revenues in
Year 2010
Percent of Firms that Already Developed Proprietary
Technology, Processes, or Procedures
6.5
8.3
1.1
Percent of Firms that Already Applied for Patent,
Copyright, or Trademark
4.9
6.0
3.3
Percent of Firms Stating That Many Existing Firms
Already Offer Same Product/Service to Expected
Customer Base
35.7
43.3
39.6
Percent of Firms Stating That No Existing Firms Offers
Same Product/Service to Expected Customers
19.2
13.3
17.3
Sample Size
1,214
602
162
Most Small Businesses Do Not Innovate
Data From the Panel Study of Entrepreneurial Dynamics
Sample 1:
All
Sample 2:
Positive
Revenues In
Year 2006
Sample 3:
Positive
Revenues in
Year 2010
Percent of Firms that Already Developed Proprietary
Technology, Processes, or Procedures
6.5
8.3
1.1
Percent of Firms that Already Applied for Patent,
Copyright, or Trademark
4.9
6.0
3.3
Percent of Firms Stating That Many Existing Firms
Already Offer Same Product/Service to Expected
Customer Base
35.7
43.3
39.6
Percent of Firms Stating That No Existing Firms
Offers Same Product/Service to Expected
Customers
19.2
13.3
17.3
Sample Size
1,214
602
162
Ex-Ante Heterogeneity in Desires
•
Most small businesses want to stay small
•
Most small businesses do not want to innovate
Most Small Businesses Report Wanting to Stay Small
Panel Study of Entrepreneurial Dynamics
All PSED
Respondents
Percent of New Firms That Report That They Want to Be “Big” *
Expected Number of Employees Working in Firm When it is 5 Years Old
(25th percentile)
Expected Number of Employees Working in Firm When it is 5 Years Old
(Median)
Expected Number of Employees Working in Firm When it is 5 Years Old
(75th percentile)
24.3
1
4
10
The PSED question reads: “Which of the following two statements best describe your
preference for the future size of this new business: I want this new business to be as
large as possible, or I want a size I can manage myself or with a few key employees?”
Most Do Not Expect to Innovate
Variable
Sample III
Sample V
% Expect To Develop Proprietary Technology/Processes
7.9
13.0
% Expect to Apply for Patent, Copyright, or Trademark
16.4
18.0
% Stating That Spending on R&D Will Be a Major Priority
16.1
19.3
Actual and Expected Innovative Activities
Why Do Businesses Start
•
“Why did you want to start this business” (from the Panel Study of
Entrepreneurial Dynamics).
Had A Good Business Idea/Create New Product
Take advantage of opportunity; High demand for products/business; Market Opportunity; untapped
market; shift in market; New technology/product/service
To Generate Income
Income; To Make Money; Extra Income
Non Pecuniary Reasons
Be own boss; tired of working for others; Flexibility ; set own hours; Stay home with children ; work
from home; Enjoy the work, have passion for it ; hobby
Other
Friend/family member had an idea and started a business; Inheritance
Lack of Employment Options
If not to innovate, why start?
Sample I
Reason For Starting Business
A.
First
Report
B.
Any
Report
Non Pecuniary Reasons
35.3
50.5
To Generate Income
19.5
34.1
Had A Good Business Idea/Create New Product
32.2
40.6
Lack of Other
Employment Options
2.2
3.8
Other
10.8
15.7
Variation in Innovation and Growth by Those Who
Start for Non Pecuniary Reasons
(1)
Coefficient
Create New
Product
(2)
Coefficient
NonPecuniary
(3)
Diff.
(2) - (1)
(4)
p-value
of
Diff.
Firms Stated That Many Existing Firms Already Offer Same Product/Service to
Expected Customer Base
-0.082
(0.035)
0.049
(0.035)
0.131
<0.01
Firm Stated That No Existing Firms Offers Same Product/Service to Expected
Customers
0.037
(0.029)
-0.049
(0.028)
-0.086
0.01
Firms Already Developed Proprietary Technology, Processes, or Procedures
0.010
(0.019)
-0.041
(0.018)
-0.051
0.01
Firms Expects to Apply for Patent, Copyright, or Trademark in Future
0.104
(0.034)
0.010
(0.033)
-0.094
0.01
Percent of New Firms That Report That They Want to Be “Big”
0.036
(0.035)
-0.047
(0.033)
-0.083
0.03
4.0
-2.0
-6.0
0.03
(2.3)
(2.3)
15.0
-10.0
-25.0
0.01
(5.3)
(5.1)
Dependent Variable
Expected Number of Employees Working in Firm When it is 5 Years Old (75th
perc.)
Expected Number of Employees Working in Firm When it is 5 Years Old (90th
perc.)
Why Do We Care: Part 1 (Academics)
•
We often write down models trying to explain the distribution of firm size
within the U.S.
•
Most models appeal to:
•
o
Liquidity constraints (some firms do not get access to credit – those that
do are the ones that grow).
o
Heterogeneity in ability which is revealed after the start up decision.
o
Luck
Our work shows that other forces are at play:
o
o
o
Most firms have no expectations/desire to grow or innovate.
Non pecuniary benefits?
Differential scale factors by industry?
Why Do We Care: Part 2 (Policy Makers)
•
Small businesses are heavily subsidized
o
o
o
o
Through tax code
Through regulation exemptions
Through loan programs
Through preferential treatment of government contracts
•
Justification of policies are usually “growth” and “innovation”
•
Are there any costs of subsidizing small businesses when “growth” and
“innovation” are not the goal of small businesses?
•
There good be:
Pulls resources from “large” business sector.
Could result in loss of agglomeration benefits
Could be regressive if non-pecuniary benefits are important.
Are non-pecuniary benefits important?
•
Cannot just rely on just survey data.
•
Explore other metrics.
•
Are people willing to take a pay cut to be self employed?
•
Hamilton (2000)
•
Moskowitz and Vissing-Jorgensen (2002)
•
Can we believe the income data reported by small business owners in
household surveys?
Part D:
“Wealth, Tastes and Entrepreneurial
Choice”
(with Ben Pugsley)
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.
65
In this paper, …..
•
We formally show that the existence of non pecuniary benefits can:
o
Inform expectations about the relationship between initial household
wealth and business entry decisions (in a world with no liquidity
constraints).
o
Inform expectations about the distribution of firm size.
o
Inform expectations about the occupations/industries where one should
see a high concentration of employment in small business firms.
o
Inform expectations about the level of non-pecuniary benefits of small
business ownership and the level of aggregate labor productivity.
o
Inform expectations about the welfare and productivity costs of small
business subsidies (including distributional implications).
A Model of Occupational Choice
w/ Non Pecuniary Benefits
•
Static general equilibrium model
•
Many households differ by initial endowment of wealth (y) and preferences for
running their own business (γ), distributed F(y,γ)
•
γ can be thought of as the size of the household’s non-pecuniary benefits from
running their own business.
•
Households have separable preferences over consumption and business
ownerships such that:
max log(C )   E
C ,E
•
Households supply labor inelastically to a common labor market or to their
own business (E).
Final and Intermediate Goods
•
Final good produced from many differentiated intermediates,b  B
 1



C    xb db 
B

•
Intermediates

 1
b  B my be supplied by firms with following technology:
fb (h)  Abh  b
•
Or, may be supplied by self employed with following technology:
fb (1)  Ab  b
•
Note:
Both sectors have same technology.
Firm sector hires labor (h) such that h > 1.
Firm’s Problem
•
A firm producing b hires labor to maximize profits and may freely enter or exit
the market.
•
Free entry pins down the size of the firm: produce at minimum efficient scale
low b
small scale firm
•
high b
larger scale firm
Assumption ensures U-shaped average cost curves for firms.
Firm Production b
Average Cost
bhigh
blow
AC(bhigh)
AC(blow)
N(blow)
N(bhigh)
N
Household’s Problem
•
Facing w (equilibrium wage) and pb (price of good b sold by firms), household
has two choices:
o Decide whether to work at a firm or run their own business.
o If running a business, what b should they produce/sell.
•
For simplicity of narrative, we assume that households can only allocate labor
to either self employed sector or the firm sector. We could allow fractional
time to both and everything would go through.
To complete the markets with this assumption, we follow Rogerson (1988) and
introduce lotteries. Basically, households choose probability E of starting a
business to maximize:
C  ( w  pb fb (1))E  w  y
(full expenditure) = (full income)
Some Comments
1.
2.
Non Pecuniary Benefits
o
Benefits come from being in a small firm (not running a firm).
o
To highlight the mechanism, we focus on an extreme notion of small
(being self employed with no employees).
o
Could easily extend this to make the non pecuniary benefits diminish with
firm size that one owns.
o
Could easily extend this to make non pecuniary benefits for being a
worker in a firm diminish with the size of the firm.
Heterogeneity in ability
o
To highlight our mechanism, we have also shut down any heterogeneity in
ability (both in self employed and firm sector).
Model Trade-Off
Benefits of small business ownership
o
Get utility of running a small business
Costs of small business ownership
o
o
o
Forgo benefits of agglomeration
Implies lower wages associated with production
The lower wages reduce utility more for individuals with low wealth.
Close the Model
•
Unit measure of households
•
Define price of final good, P:
 1 
P    pb db 
B

•
•
1
1
Normalize P = 1
Conditional demand functions:
p 
xb ( pb )  PC  b 
P

Two Sector Competitive Equilibrium
Given a household distribution Ψ(y|γ)F(γ), a two-sector competitive equilibrium is:
1.
A small business sector b < b*, and a firm sector b ≥ b*
2.
Households with wealth y > y1(γ) run the business
3.
Given y and γ, probability Eyγ of starting a business:
P{Start Business | y, γ}
0

 (0,1)
1
 
if y < y1 ( )


if y1 ( )  y  y2 ( ) 

if y  y2 ( )

4.
Entrepreneur households indifferent over b < b* and income pbfb(1) = z for all
b < b*.
5.
Wage gap w-z > 0 is the pecuniary opportunity cost of running a business.
Implication 1: Small Business Sector
Concentrated in a Few Industries
•
Consistent with the data shown above
•
Those goods that are produced using a low returns to scale technology will be
dominated by very small firms.
•
The more important are non pecuniary benefits, the more industries dominated
by small firms.
Implication 2: Skews the distribution of firms towards
smaller firms
Implication 3: Wages and Productivity
Implication 4: Wealth and Business Ownership
Implication 4: Wealth and Business Ownership
Importance:
o
HUGE literature using the relationship between wealth and entry into
business ownership as evidence of liquidity constraints.
Cagetti and DeNardi ; Buera ; Evans and Jovanovic ; Quadrini
o
The existence of non pecuniary benefits can undermine this type of
empirical strategy to test for the importance of liquidity constraints as a
deterrent to small business formation.
o
Very few of the wealthy guys that start the own business look
entrepreneurial (wineries, doctors, lawyers, accountants, etc.) (Hurst and
Lusardi 2004)
o
Can use this moment for calibration (at some later date)
Implication 5: Wages and Firm Size
Model predicts:
Small business owners will earn less than wage workers,
wage workers in the firm.
Intuition:
Some of the compensation will be taken in the nonpecuniary benefits.
Importance:
Brown and Medoff (1989) – wages of employees in
large firms are higher than wages of employees in small
firms – all else equal.
Explanation – technology based (big firms are more
productive and attract more productive workers).
A Policy Experiment
•
In order to assess the total and distribution impacts of small business subsidies,
need to add a government sector to the model.
•
Governments provide subsidy s to small business “output”.
•
Fund the subsidy with lump sum taxes, T (to start).
•
Amend the budget constraint of households and add a balanced government
budget constraint:
Implication 6: Small Business Subsidies and Aggregate
Productivity
Implication 7: Small Business Subsidies and
Distributional Impacts
Implication 7a: Small Business Subsidies and
Distributional Impacts
•
Effect of increasing subsidy on welfare by income type – compensated
differential (10% subsidy). Subsidy funded by proportional wealth tax.
EV w y
0.010
0.008
0.006
0.004
0.002
y
1
0.002
2
3
4
5
What’s Next
•
Add dynamics to the model (look at firm growth).
•
Think about heterogeneity in ability – not sure if we want to put that in
this model or not.
•
Try to calibrate the model to existing data (relationship between wages and self
employment ; relationship between wealth and self employment entry ; firm
growth dynamics overall ; firm growth dynamics by industry, etc.).
Conclusions (part D)
•
Most small businesses do not resemble the entrepreneurs in our models
(concentrated in a few industries, do not grow, do not innovate, etc.).
•
Need to think hard about using the universe of small businesses in household
data or firm data to test theories about entrepreneurship.
•
Many small business owners report non-pecuniary benefits as a being a
primary driver of small business formation.
•
Need to take such non-pecuniary benefits seriously when thinking about
testing theories of small business formation, thinking about the distribution of
firm size, or thinking about subsidizing small business formation.
•
Quantitative importance is an open question.
Part E:
“Are Household Surveys Like Tax Forms:
Evidence From the Self Employed”
(with Geng Li and Ben Pugsley)
Things We Know
•
Individuals are very willing to lie about their income to tax authorities.
- More likely to lie when tax rates are high or income is high.
- Mostly done by the self employed (no W2’s reported)
- Underreporting can be large! (50% in recent random audits).
•
Participants in experiments will distort their behavior as a reaction to
being studied.
- Large literature on the Hawthorne Effect
•
Participants are willing to lie to collectors of other “administrative data”.
- Age of first marriage in Vital Statistics data (Blank et al. 2009)
Assumptions We Make
•
Most researchers, however, assume that individuals or groups of individuals do
not systematically misreport information to household surveys.
o
o
We know there is measurement error in household surveys (recall bias).
We know there is noise in household surveys (assume it is classical).
•
Yet, we assume the problems that plague tax data, economic experiments, and
administrative data do not plague household survey data.
•
On the one hand:
o
•
On the other hand:
o
o
Why would people lie to household surveys?
Benefits are small
Why would people not lie to household surveys?
Cost of misreporting is zero
Costs may be positive of not misreporting
What This Paper Addresses
•
•
Are household surveys akin to tax forms?
o
Do the same problems that plague tax, administrative and experimental
data, plague household surveys in that people will misreport information
when it is in their incentive to do so? (Yes)
o
If so, by how much? (25%-35%, depending on specification)
Focus on the income reporting of the self employed
o
Why? Large evidence that self employed misreport their income to the
tax authorities.
o
Self employed are a large group. Their misreporting may lead to biases in
a wide range of empirical studies. (It is important in variety of settings)
Data
1.
PSID (1980 – 1997, excluding years where food data was omitted)
o
o
2.
Food expenditure
Detailed income measure
CEX (1980 – 2003)
o
o
Detailed expenditure measures
Some income measures.
Main income measures: Head and Spouse, Wage/Salary + Business Earnings
Total family money
Self employed measures: Self reported
Sample:
Male heads, aged 25-55, working full time (at least 30
hours a week; worked 40 weeks during previous year).
Conditional Income Differences, By Sample
Data Sets/Specification
Income Measure:
Labor Earnings + Business Income
(4)
(5)
(6)
Variables
Self Employed Dummy
-0.07
(0.02)
0.03
(0.01)
-0.07
(0.01)
Demographic Controls
Year Dummies Included
Yes
Yes
Yes
Yes
Yes
Yes
Sample
CE
PSID
CPS
log yit  0y  1y Dit   y ' X it  ty ity
Conditional Expenditure Differences, By Sample
Data Sets/Dependent Variable
I. CE
II. PSID
Variables
Food
Non Durable
Total
Food
Dummy: Self Employed
0.14
(0.01)
0.15
(0.01)
0.18
(0.01)
0.15
(0.01)
Demographic Controls
Year Dummies Included
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
log cit     Dit   ' X it    ,
c
0
c
1
c
c
t
c
it
Testing for Underreporting of Income: A Model
ln cikt  k  k ln y  k ' X ikt  kt  ikt
p
ikt
1.
Assume a log linear Engel Curve between expenditures and
permanent income
•
k indexes wage and salary workers (W) and self employed (S)
(3)
ln yikt  ln y   ' X ikt  ikt ,
*
p
it
• Reported income (y*) is a function of permanent income and either transitory
variation or classical measurement error.
• Estimating (3) with OLS will yield an attenuated estimate of β.
2.
Assume education dummies is a good instrument for permanent
income in (3).
Engel Curve: IV Estimates of (1), CEX Data,
Food Expenditures
Worker
Entre
9.5
9
8.5
8
7.5
9.5
10
10.5
11
11.5
12
Engel Curve: IV Estimates of (1), PSID Data,
Food Expenditures
Worker
Entre
9.5
9
8.5
8
9
9.5
10
10.5
11
11.5
12
12.5
Engel Curve: IV Estimates of (1), CEX Data,
Non Durable Expenditures
Worker
Entre
11
10.5
10
9.5
9
8.5
8
9.5
10
10.5
11
11.5
12
Testing For Underreporting
ln yiWt  ln y
*
p
iWt
 W ' X iWt  iWt
ln yiSt  ln S  ln y  S X iSt  iSt
*
p
iSt
3. Assume self employed misreport their income by κ percent relative
to wage and salary workers
Estimating Regression
*
ln cikt     log yikt
  DitS  X ikt  t  ikt
(8)
Additional Assumptions (we will provide support for these shortly)
4.
Self employed do not underreport their expenditures relative to
wage/salary workers
5.
Self employed have same “preferences” as wage and salary worker
(parameters of log linear Engel curves are otherwise the same between
the two groups).
Given above assumptions:
ˆ  exp(ˆ / ˆ )
Main Results
Sample/Expenditure Measure
Income = Total Family Income (IV Specification)
Estimated
Estimated
β
γ
CEX, Non Durable Expenditure
0.60
(0.01)
0.18
(0.01)
CEX, Total Expenditure
0.76
(0.01)
0.21
(0.01)
CEX, Food Expenditure
0.40
(0.01)
0.15
(0.01)
PSID, Food Expenditure
0.32
(0.01)
0.11
(0.01)
Main Results
Expenditure Measure
Estimate of (1-κ), in Percent
Income =
Income =
Total Family
Labor Earnings Plus
Income
Business Income
CEX, Non Durable Expenditure
25.5
(1.5)
27.5
(1.6)
CEX, Total Expenditure
24.5
(1.5)
26.5
(1.5)
CEX, Food Expenditure
31.7
(2.0)
33.5
(2.0)
PSID, Food Expenditure
30.1
(2.2)
34.9
(1.9)
Underreporting of income by self employed range from 25% -35%
Robustness (Issue #1)
•
Misreporting of expenditures by self employed?
o
Use different measures of expenditure where we think underreporting is
less of an issue.
o
For example, estimated underreporting using home utility expenditures
equals 30%.
o
Results similar for all expenditure categories aside from nondurable
transportation expenditures.
o
Estimate a complete demand system analysis to test for whether there
appears to be systematic underreporting of expenditures across all
categories.
o
No evidence of systematic underreporting of expenditures.
Robustness (Issue #2)
•
Different Income Concepts of Self Employed
o
What if the income concepts differ between the self employed and
wage/salary workers.
o
Retained earnings may not be counted as income but they are conceptually
equivalent to savings.
o
Can we rule out that unmeasured retained earnings are driving our results?
o
Yes – (for the most part).
- A large fraction of self employed report no business wealth at all.
- Estimate the specifications for those with zero business wealth and
more than $10,000 of business wealth.
- No major differences across the groups.
Robustness (Issue #3)
•
Differences in preferences/expecations/liquidity constraints?
o
Hard to systematically address.
o
Some good news: slopes of the Engle curves are the same.
o
What to see if anything else cause self employed to save more or less for a
given level of income.
-
Binding liquidity constraints (more saving)
More uncertainty (more precautionary saving)
Difference in risk preferences (less precautionary saving)
Higher expected income path (less saving)
Difference in home production (more expenditures)
Robustness (Issue #3)
•
Solutions?
o
Look at different age ranges.
-
Budget constraints still must hold – if save more now, should
spend more later.
-
If save for precautionary reasons, should spend as risk
diminishes.
o
Control for work hours (different taste for leisure, differences in home
productions).
o
Test for differences by wealth quartile.
-
Find no difference in estimated underreporting within each
wealth quartile.
Robustness
Expenditure Measure
1
Specification/Sample
2
3
4
CEX, Non Durable Expenditure
25.2
(1.5)
27.4
(1.6)
26.6
(2.7)
24.4
(1.8)
CEX, Total Expenditure
24.5
(1.5)
26.7
(1.5)
24.2
(2.6)
24.6
(1.7)
CEX, Food Expenditure
31.5
(2.0)
33.5
(1.9)
35.7
(3.7)
28.6
(2.2)
PSID, Food Expenditure
28.6
(2.2)
34.1
(1.8)
30.1
(3.0)
28.4
(3.0)
Income Measure
Total
Labor Plus
Business
Total
Total
Restrict To
Heads With
Age 25-40
Restrict To
Heads With
Age 40-55
Specification
Include Log
Include Log
Work Hours as Work Hours as
a Control
a Control
Robustness (Issue #4)
•
Should we be using after-tax income?
o
Self employed underreport income to tax authorities.
o
For a given pre-tax income, have more money to allocate to expenditures
because they are saving on tax liabilities.
o
Can test for this directly. CEX asks households to report their outlays on
federal, state, and local taxes.
-
Create a measure of after-tax income.
-
Redo our analysis with after-tax income.
-
Estimated under-reporting declines slightly (0.21-0.28).
Differences Over Time
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1980
-0.1
1985
1990
1995
2000
Variation Across Tax Regimes and Demographics
I. CE
(1)
Non Durable Expend.
(2)
Food Expenditure
II. PSID
(3)
Food Expenditure
Avg marginal tax rate (%)
2.24
(0.89)
2.76
(1.18)
1.56
(0.84)
Age: 40-55
1.91
(3.86)
0.62
(5.32)
3.45
(4.63)
Ed = Some College
4.94
(5.15)
-5.62
(5.08)
6.04
(6.72)
-3.03
(6.81)
14.51
(4.74)
2.20
(6.16)
Ed = More than College
-11.48
(6.00)
-2.89
(7.47)
-23.03
(9.74)
Black
10.39
(16.30)
-8.19
(5.47)
2.98
(18.43)
-21.05
(8.63)
6.03
(11.25)
-4.11
(9.39)
Total Family
Total Family
Total Family
Variable
Ed = College
Married
Income Measure
Why Does This Matter? 5 Examples
1.
Earnings differentials between self employed and wage/salary workers.
Hamilton (JPE, 2000):
Self employed earn 35% less than wage/salary
workers
o
Does not account for underreporting of income by self employed.
o
Our results nearly undoes all of Hamilton’s results.
o
Note:
Risk adjusted and accounting for fringe differences, wage/salary
workers still earn more.
Why Does This Matter? 5 Examples
2.
Wealth Differences Between Self Employed And Wage/Salary Workers
•
Lots of work on this. Used to provide evidence that liquidity constraints are
binding for self employed (business owners, enterpreneurs).
•
o
Wealth of self employed, conditional on observables including income, is
much higher than wealth of wage/salary workers.
o
Underreporting of income by self employed will lower these differentials.
How much does this matter?
o
Use PSID sample:
Conditional log wealth differentials ~ 0.9.
o
Account for underreporting:
Conditional log wealth differentials ~ 0.6.
Why Does This Matter? 5 Examples
3.
Importance of Precautionary Savings
o
Carroll and Samwick (1997, 1998) show that roughly 50% of the wealth
holdings of individuals under the age of 45 is due to precautionary
motives.
o
Regress wealth on measures of risk, average measures of income, and
demographics.
o
Self employed have higher risk and higher wealth.
o
However, they also underreport their average income.
o
Scaling up their income accordingly reduces Carroll/Samwick
estimates by 13% (from 47.5% to 41.1%).
Why Does This Matter? 5 Examples
4.
Lifecycle Earnings
o
Self employment rate changes over the lifecycle (by roughly 15 percentage
points between age 25 and 65, 10 percentage points between 45 and 65).
o
As a result, systematic measurement error differs across the lifecycle.
o
How much of the decline in income after middle age is due to increased
underreporting of income?
o
About 9% of the decline in earnings between 45 and 65 is due to
misreporting of income by self employed. ( 3 percentage points out of the
35 percentage points decline).
-
Comparable numbers: ~5% in PSID and ~13% in CPS.
Why Does This Matter? 5 Examples
5.
Spatial Differences in Earnings
o
Self employment propensities differ across space (Glaeser 2009). (Mean self
employment rate across U.S. MSAs in 2000 census = 0.125 (standard error =
0.029)).
o
To the extent that self employed underreport their income, spatial differences
in income could be biased.
o
Some examples (using 2000 census)
-
Using reported income, Nassau County (NYC) had 8.9% higher
earnings than Detroit. However, after our adjustment, the difference
was 10.6%.
-
Likewise, unadjusted San Fran had 23.9% higher income than
Buffalo. With the adjustment, that difference was 25.5%.
Conclusions (Part e)
•
Survey data faces the same potential problems with respect to systematic
measurement error as other types of data (tax data, other administrative data,
experimental data, etc.).
•
We focus on the self employed.
•
Self employed underreport their income by ~25% in household surveys.
•
Some evidence of variation with tax regimes and education levels (although, it
is weak).
•
Not accounting for the underreporting biases many types of empirical works.
The extent of the quantitative importance depends on the application.
•
It is naive to assume that individuals will automatically provide accurate
information to household surveys when they are simultaneously providing
distorted information to other sources.