Transcript DANISH NEIGHBOURS AS NEGATIVES

Self-Employment, Well-Being, Rents
and Matching
Andrew E. Clark (Paris School of Economics and IZA)
http://www.parisschoolofeconomics.com/clark-andrew/
APE/ETE Masters Course
TWO KEY QUESTIONS
1) Is self-employment (SE) a choice, or is it
imposed because there are no better options?
2) Are SE jobs better than employment? And if
so, why aren’t we all SE?
Question 2) brings into play the distinction
between rents and sorting/matching; the latter
being based on worker heterogeneity (utility
or productivity).
The History of SE in OECD Countries: Rates are Falling
Australia
Austria
Belgium
Canada
Czech Republic
Denmark
Finland
France
Germany
Greece
Hungary
Iceland
Ireland
Italy
Japan
Korea
Luxembourg
Mexico
Netherlands
New Zealand
Norway
Poland
Portugal
Slovak Republic
Spain
Sweden
Switzerland
Turkey
United Kingdom
United States
1990
15.9
14.2
18.1
9.5
..
11.7
15.6
13.2
10.9
47.7
..
15.1
24.9
28.7
22.4
39.5
9.4
31.9
11.6
20.0
11.3
27.2
29.4
..
25.9
9.2
..
61.0
15.1
8.8
1995
15.9
14.4
18.8
10.6
12.0
9.6
15.6
10.8
10.7
46.1
18.0
19.6
22.2
29.3
18.3
36.8
7.6
40.9
12.4
21.0
9.4
29.7
27.9
6.5
25.2
11.2
12.7
58.5
14.6
8.5
2000
14.5
13.1
..
10.7
15.2
8.7
13.7
9.2
11.0
41.6
15.2
18.0
18.9
28.5
16.7
36.8
7.3
36.4
12.0
20.8
7.4
27.4
26.5
8.0
20.1
10.3
13.2
51.4
12.3
7.4
2003
13.4
12.8
..
9.7
17.3
8.8
12.9
8.8
11.4
..
13.5
..
17.5
27.5
15.2
34.9
6.8
37.1
..
19.3
7.3
27.3
26.8
9.8
18.6
9.6
11.9
49.4
12.7
7.6
Wide disparity between countries
Self-employment rates: total
As a percentage of total civilian employment, 2003
60
50
40
30
20
10
0
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Men are more likely to be SE than are women
As a percentage of total civilian employment, 2003
Self-employment rates: men
50
40
30
20
10
0
As a percentage of total civilian employment, 2003
Self-employment rates: women
60
50
40
30
20
10
0
Job Characteristics: SE vs E.
Wages. WSE < WE. And wage growth lower for
SE than for E.
Issue of self-selection (panel) for wage levels
Wage growth might show
- Incentive contracts for E (Akerlof and Katz)
- Workers learning quality of E job match over
time, and quitting low-quality matches
- SE requires higher levels of human K. Returns
to latter are concave.
Job Characteristics: SE vs E.
Hours. ESS data.
E: 40 hours per week (including OT)
SE: 51 hours per week
Job Security. You can’t sack yourself…but then
again firms can insure you (an implicit
contract).
BHPS. Satisfaction with job security (1-7 scale)
Employees
= 5.30
Self-Employed = 5.08
T-statistic = 11 for the difference in means
Job Characteristics: SE vs E.
Risk.
dW/dShock is three times larger for the SE than
for E.
There is therefore less insurance for the SE (as
utility functions are concave)
Autonomy.
This is obviously where the SE win.
Sociability.
SE are often on their own.
Measuring Well-being: The Day
Reconstruction Method
Respondents reconstruct the previous day.
Split into a sequence of episodes.
Respondents report the key features of each
episode, including
(1) When the episode began and ended
(2) what they were doing
(3) where they were
(4) Whom they were interacting with, and
(5) how they felt on multiple affect dimensions
For each of the episodes that individuals identify during the
day, they are asked the following questions:
Mean affect rating
Activities
Intimate relations
Socializing
Relaxing
Pray/worship/meditate
Eating
Exercising
Watching TV
Shopping
Preparing food
On the phone
Napping
Taking care of my children
Computer/e-mail/Internet
Housework
Working
Commuting
Interaction partners
Friends
Relatives
Spouse/SO
Children
Clients/customers
Co-workers
Boss
Alone
Duration-weighted mean
% time > 0
Mean
Proportion
hours/day of sample
reporting
Positive
Negative
Competent Impatient
Tired
5.1
4.59
4.42
4.35
4.34
4.31
4.19
3.95
3.93
3.92
3.87
3.86
3.81
3.73
3.62
3.45
0.36
0.57
0.51
0.59
0.59
0.5
0.58
0.74
0.69
0.85
0.6
0.91
0.8
0.77
0.97
0.89
4.57
4.32
4.05
4.45
4.12
4.26
3.95
4.26
4.2
4.35
3.26
4.19
4.57
4.23
4.45
4.09
0.74
1.2
0.84
1.04
0.95
1.58
1.02
2.08
1.54
1.92
0.91
1.95
1.93
2.11
2.7
2.6
3.09
2.33
3.44
2.95
2.55
2.42
3.54
2.66
3.11
2.92
4.3
3.56
2.62
3.4
2.42
2.75
0.2
2.3
2.2
0.4
2.2
0.2
2.2
0.4
1.1
2.5
0.9
1.1
1.9
1.1
6.9
1.6
0.11
0.65
0.77
0.23
0.94
0.16
0.75
0.3
0.62
0.61
0.43
0.36
0.47
0.49
1
0.87
4.36
4.17
4.11
4.04
3.79
3.76
3.52
3.41
3.89
97%
0.67
0.8
0.79
0.75
0.95
0.92
1.09
0.69
0.84
66%
4.37
4.17
4.1
4.13
4.65
4.43
4.48
3.76
4.31
90%
1.61
1.7
1.53
1.65
2.59
2.44
2.82
1.73
2.09
59%
2.59
3.06
3.46
3.4
2.33
2.35
2.44
3.12
2.9
76%
2.6
1
2.7
2.3
4.5
5.7
2.4
3.4
0.65
0.38
0.62
0.53
0.74
0.93
0.52
0.9
Source: Kahneman, D., Krueger, A., Schkade, D., Schwarz, N., & Stone, A. (2004). "A Survey Method for Characterizing Daily
Life Experience: The Day Reconstruction Method". Science, 3 December 2004, 1776-1780.
Job Characteristics: SE vs E.
Overall Conclusion.
SE do worse than E by almost all of the counts.
Old question in labour: how can we add up the different
domains to produce an overall index of job quality?
My answer: We might not need to. Let’s ask individuals
to do it for us by reporting their own evaluation of
their job: their job satisfaction.
Job SatisfactionSE > Job SatisfactionE
• In raw data
• With controls in (pooled) cross-section
• And mostly in panel analysis too
Job Characteristics: SE vs E.
This looks like a mystery.
SE do worse than E by almost all of the counts.
But they’re more satisfied…
Maybe we shouldn’t believe satisfaction scores,
but instead ask a direct hypothetical preference
question. This one comes from the “Work
Orientations” module of the ISSP:
“Suppose you were working and could choose
between different kinds of jobs. Which of the
following would you personally choose?”
Percentage of Working who
are Self-Employed
West Germany
Great Britain
USA
Hungary
Norway
Sweden
Czech Republic
New Zealand
Canada
Japan
Spain
France
Portugal
Denmark
Switzerland
1989
11.0%
11.7%
12.1%
5.9%
5.1%
1997
11.9%
15.2%
13.4%
14.5%
9.8%
10.7%
10.6%
9.2%
15.2%
16.8%
3.4%
8.9%
23.6%
6.5%
12.1%
2005
10.4%
12.9%
13.3%
9.0%
10.9%
10.3%
14.9%
15.1%
8.6%
11.4%
14.3%
8.4%
14.1%
8.5%
10.1%
Percentage of Working who
Prefer Self-Employment to
Employment
1989
1997
2005
51.4%
61.7%
44.3%
49.6%
46.2%
48.7%
63.5%
72.3%
64.4%
42.2%
58.8%
39.1%
26.6%
27.5%
28.4%
38.0%
31.8%
42.8%
30.7%
63.4%
55.0%
58.7%
55.6%
42.7%
33.4%
42.9%
33.9%
42.7%
40.6%
76.3%
51.8%
26.1%
28.4%
65.6%
47.2%
The SE are therefore more satisfied than the employed,
and the percentage saying they would prefer to be
SE is systematically three to four times higher than
the percentage who actually are.
How can we have USE > UE in equilibrium?
Three possible explanations
•
•
•
Capital constraints
Matching by Know-How
Matching by Risk-Aversion
1) Capital constraints
As epitomised in Blanchflower and Oswald. Journal of Labor Economics
(1998)
Being SE requires capital. Not all SE have enough to set up on their own and
have to borrow. Asymmetric information between entrepreneurs and
banks: the latter cannot evaluate how good the entrepreneur’s project is.
As a result, some profitable projects may not be funded.
Two possibilities
•
If the market clears, then USE = UE
•
If the market does not clear, then USE > UE
In the latter case, the utility gap should fall with entrepreneurs’ own capital.
The more entrepreneurs are able to self-finance their projects, the less
banks matter, and the smaller is the utility gap.
Formal model in Evans and Jovanovic (1989)
Household Choice:
Become a worker:
Earn wage:
(wζ)
Become an “entrepreneur”:
Earn income:
( y   k  )
where: θ is entrepreneurial ability (known 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.
Static model: People are endowed with initial wealth z.
Evans and Jovanovic (1989)
Total 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).
Choice of Optimal Entrepreneurial 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

Finish Solving The Model: Part 1
Entrepreneurial Income as a function of constrained/unconstrained k.
Finish Solving the Model: Part 2
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 ) 


Implication of the Model:
Probability of Entrepreneurship Increasing in Wealth
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.
• Smaller firms will grow faster; once they reach the unconstrained region assets
no longer increase investment in the business
• Increasing θ won’t increase SE if z is low enough
• Subsidising borrowing won’t increase SE if θ is low enough
Empirical Test in Blanchflower and Oswald
NCDS Data. Covers all GB children born between the 3rd and 9th
of March 1958. Surveys carried out when children were aged
7, 11, 16, 23, 33 and 42.
NCDS at ages 23 and 33 used. Percentage of SE rises from 6%
(1981) to 14% (1991) – life cycle and macro effects.
Key variable measures capital constraints: did the respondent
receive an inheritance of > £500?
Bivariate evidence. At age 33:
•
•
•
14% of those without an inheritance were self-employed
22% of those with an inheritance of £10K-£20K were selfemployed
33% of those with an inheritance of £50K+ were selfemployed
Regression for P(SE)
P(SE) rises with inheritance. Col. 4 instruments for inheritance via death of parents.
Shows the importance of capital constraints.
There is also direct evidence. 50% of the
employed who had thought about becoming
SE (but didn’t) cite lack of capital (BSA data)
Blanchflower and Oswald also look at job
satisfaction.
Job satisfaction is higher for the SE. But only for the SE without
inheritance. The SE with inheritance are just as satisfied as
employees (as if the labour market cleared for them). This is
consistent with capital constraints.
Job Satisfaction might go up… but life satisfaction go
down (job really great, but spend no time at home and
no leisure). Check via life satisfaction.
2) Intellectual Capital or “Know-How”
Based on work by Masclet and Colombier.
Again, intergenerational transmission: but this time of
ability which affects individual productivity when
they are self-employed.
Productivity when self-employed, θ, partly comes from
one’s parents.
Data from the French component of the ECHP (19942001), aged 18-64. Gives 45,000 observations on E
and 5,500 on SE (self-employment rate of 12%).
P(SE) rises with inheritances, as in 1), and with own human capital
(education). But also rises with parents’ SE status, and especially if
parents were SE in the same profession. The effect is stronger for men
than for women.
Note that this is a matching story, and does not reflect rents… in
the sense that those who are E do not want to become SE.
Linear utility function
U= αw - βR;
Where w is wages and R is the disutility of work.
This is the same for all workers.
However workers do differ in their productivity when self-employed.
Earnings when self-employed are θAwSE and θBwSE
We assume that θB > θA: the B’s are better at self-employment than the A’s (because they had
human capital transmitted to them by their parents).
Earnings when employed are wE for both A’s and B’s
There is disutility of work of RE and RSE in the two sectors.
The A’s (not good at SE) will choose E if
αwE - βRE > αθAwSE - βRSE
which gives
αθAwSE < αwE + β(RSE - RE)
or
wSE < 1/θA * wE + β/αθA * (RSE - RE)
(1)
In the same way, the B’s will choose SE if
αθBwSE - βRSE > αwE - βRE
which gives
wSE > 1/θB * wE + β/αθB * (RSE - RE)
(2)
Note that we can write (1) and (2) as
wSE < 1/θA * X
and
wSE > 1/θB * X
As we have assumed that θB > θA, then 1/θB < 1/θA and there is a range for wSE in which both
(1) and (2) hold. In this case there will be sorting on the labour market.
Note:
i)
UA = UB if both E, because there is no difference in the utility function, or
productivity when employed (U= αwE - βRE for both A and B).
ii)
UB > UA if both SE, because the B’s are more productive, and earn more.
iii)
In the sorting equilibrium:
UAsort = αwE - βRE
UBsort = αθBwSE - βRSE
Which group has the highest utility in the sorting equilibrium?
UBsort > UAsort if:
αθBwSE - βRSE > αwE - βRE
which gives
wSE > 1/θB * wE + β/αθB * (RSE - RE)
But this is exactly the same as (2)! So whenever there is sorting, B’s do better.
We therefore have a sorting equilibrium in which UB > UA: the self-employed have higher
well-being, but the employed still prefer employment (they would have even lower utility if
we forced them to become self-employed).
3) Risk-Aversion
Are the self-employed less risk-averse than the employed?
1)
Survey evidence from the GSOEP in 2004 (Dohmen et al.).
22 000 individuals asked about “willingness to take risks” in
different domains. Scale of 0 to 10: 0 = “unwilling to take
risks” and 10 = “fully prepared to take risk”
Risk Type
General
Car Driving
Financial Matters
Career
Health
SE Coefficient
0
0
+ve
+ve
0
2) Survey evidence from Finland (Ekelund et
al., Labour Economics, 2005). 1966 Birth
Cohort Study.
Questionnaire measure of harm avoidance (7
questions on worry and risk): 1-7 scale.
Formalise via a probability of selfemployment equation.
The coefficient of 0.100 (roughly) means that moving from 1 to 7
on the risk-aversion scale produces a change in the likelihood of
self-employment as large as that between men and women.
3) Experimental. This involves far smaller N,
but real decisions (Colombier et al., Journal
of Economic Behavior & Organization).
Holt-Laury measure of risk via lotteries.
Individuals choose between two lotteries, A and
B. The key element here is that lottery B is
riskier than is lottery A.
For the first choices, the EV of A is greater than that of
B; as the probabilities of winning the larger amount
increase, the EV of B finally becomes greater than
that of A.
The point where individuals change between A and B
shows their risk-aversion.
Someone who is RN chooses according to EV: they
choose A for the first four choices, and then B
thereafter.
Someone who is RA will change later. At choice 5 the
EV of B is greater than that of A, but the RA will
still go for A (because they are scared of getting the
small prize, 0.1, in lottery B)
Someone who is RL will change earlier.
Main Result: the (real-life) E are more risk-averse than the (real-life) SE
All three explanations are consistent with USE > UE.
The first is a rent story; the second two are matching.
Apply these results to two empirical phenomena.
•
SE rates have been falling
•
France is not entrepreneurial
The SE decision is based on the comparison of the value of VSE to VE.
1) Jobs have been getting of better quality (?) and French jobs are really good
(??).
2) Constrained access to employment, so choose SE. So unemployment has
been falling (Yes) and France has low unemployment (No)
3)
4)
5)
6)
VSE has been falling because tastes have changed: increasing
taste for leisure (SE hours higher) or increasing taste for
income (SE income lower).
Capital constraints have increased, and are particularly large
in France.
Sorting: less know-how handed down (because jobs change
so quickly now??) and less know-how in France. But that
only explains low French SE now by low French SE in the
past….
Sorting: Risk-aversion has been rising, and the French very
risk-averse.
I like no. 4), but the analysis of self-employment, particularly
cross-country, is still wide open for further research.
In particular, beware of the dreaded “OECD-country generality”.
This assumes that “any result I’ve found in the UK must
necessarily generalise worldwide”.
This point is really well brought out in Bianchi (2010). Financial
development eases the capital constraints to becoming selfemployed. That’s what we have already understood.
However, it does something else as well: it affects both the classic
labour market and the product market. The satisfaction
differential between the self-employed depends on three
things:
i)
SE profit
ii) SE non-pecuniary return (value of autonomy)
iii) Employed wages.
Financial development affects all three, especially in developing
countries).
Financial Development and Job Satisfaction
Dependent Variable: Job Satisfaction
Low FD
High FD
Low FD
High FD
(2)
(3)
(4)
(5)
Low FD
(6)
High FD
(7)
Sample
Full
(1)
FD*SE
0.3091**
(0.1390)
0.8217**
(0.3476)
0.0747
(0.2238)
0.9113**
(0.3576)
-0.0249
(0.2366)
0.4510
(0.3431)
0.1818
(0.2117)
GDP*SE
0.0101**
(0.0047)
0.0120
(0.0074)
0.0063
(0.0056)
0.0153*
(0.0079)
0.0090
(0.0058)
0.0136
(0.0087)
0.0124***
(0.0045)
0.1142***
(0.0135)
0.0944***
(0.0145)
0.3287***
(0.0123)
0.3513***
(0.0124)
Income
Independence
SE
0.0426
(0.0916)
-0.1703
(0.1370)
0.3164
(0.2067)
-0.2866**
(0.1347)
0.3645*
(0.1858)
-0.7240***
(0.1442)
-0.6136***
(0.1873)
Female
0.0077
(0.0258)
0.0262
(0.0389)
-0.0126
(0.0293)
0.0325
(0.0433)
-0.0170
(0.0292)
0.1444***
(0.0336)
0.0772**
(0.0308)
Age
0.0026
(0.0044)
0.0080
(0.0072)
-0.0026
(0.0061)
0.0056
(0.0082)
-0.0037
(0.0061)
-0.0155**
(0.0062)
-0.0241***
(0.0067)
Age-sq
0.0002***
(0.0000)
0.0001
(0.0001)
0.0002***
(0.0001)
0.0001
(0.0001)
0.0002***
(0.0001)
0.0003***
(0.0001)
0.0004***
(0.0001)
Married
0.1905***
(0.0283)
0.1951***
(0.0383)
0.1848***
(0.0371)
0.1175**
(0.0454)
0.1012**
(0.0414)
0.1409***
(0.0331)
0.1070***
(0.0360)
Education
0.0312***
(0.0072)
0.0392***
(0.0106)
0.0238**
(0.0104)
0.0198*
(0.0099)
0.0036
(0.0083)
-0.0179**
(0.0087)
-0.0181**
(0.0079)
Country*Year
Fixed Effects
YES
YES
YES
YES
YES
YES
YES
Observations
R-squared
45996
0.08
23359
0.09
22637
0.07
20526
0.10
18976
0.08
23107
0.23
22519
0.24
Another factoid: Self-employment is more satisfactory than
employment… and becoming more so
Self-Employment
Self-Employment*1997
Self-Employment*2005
1989-2005
0.327**
(0.034)
1989-2005
0.377**
(0.062)
-0.076
(0.081)
-0.061
(0.085)
1997-2005
0.353**
(0.023)
1997-2005
0.302**
(0.032)
0.108*
(0.046)
This is consistent with entry barriers to self-employment rising over time:
i) The self-employment rate is falling;
ii) More people want to be self-employed than are actually selfemployed; and
iii) The satisfaction “premium” from self-employment is on the rise