Self-Employment, Well-Being, Rents and Matching

APE/ETE Masters Course

Andrew E. Clark (Paris School of Economics and IZA)http://www.parisschoolofeconomics.com/clark-andrew/

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

1990 2000 2005 2010Australia 14.4 13.6 12.7 11.6

Austria 14.2 13.1 13.3 13.8

Belgium 18.1 15.8 15.2 14.4

Canada 9.5 10.6 9.5 9.2

Chile .. 29.8 30.4 26.5

Czech Republic .. 15.2 16.1 17.8

Denmark 11.7 8.7 8.7 8.8

Estonia .. 9.1 8.1 8.3

Finland 15.6 13.7 12.7 13.5

France 13.2 9.3 9.1 ..

Germany .. 11.0 12.4 11.6

Greece 47.7 42.0 36.4 35.5

Hungary .. 15.2 13.8 12.3

Iceland .. 18.0 14.2 12.6

Ireland 24.9 18.8 17.7 17.4

Israel .. 14.2 13.1 12.8

Italy 28.7 28.5 27.0 25.5

Japan 22.3 16.6 14.7 12.3

Korea 39.5 36.8 33.6 28.8

Luxembourg 9.1 7.4 6.5 ..

Mexico 31.9 36.0 35.5 34.3

Netherlands 12.4 11.2 12.4 ..

New  Zealand 19.8 20.6 18.3 ..

Norw ay 11.3 7.4 7.4 7.7

Poland 27.2 27.4 25.8 22.8

Portugal 29.4 26.0 25.1 22.9

Slovak Republic .. 8.0 12.6 16.0

Slovenia .. 16.1 15.1 17.3

Spain 25.8 20.2 18.2 16.9

Sw eden 9.2 10.3 9.8 10.9

Sw itzerland .. 13.2 11.2 ..

Turkey 61.0 51.4 43.0 39.1

United Kingdom 15.1 12.8 12.9 13.9

United States 8.8 7.4 7.5 7.0

EU27 total .. 18.3 17.3 ..

OECD total .. 17.7 16.8 ..

Russian Federation .. 10.1 7.8 6.9

Wide disparity between countries

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In 2010, the share of self-employed workers in the total (men and women together) ranged from under 8% in the United States, and Norway to well over 30% in Greece, Mexico, and Turkey.

In general, self-employment rates are highest in countries with low per capita income although Italy, with a self-employment rate of around 25.5%, is an exception. Ireland and Spain also combine high per capita incomes and high self-employment rates.

Men are more likely to be SE than are women

Self-employment rates: womenAs a percentage of total civilian employment, 2003

Self-employment rates: menAs a percentage of total civilian employment, 2003

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Job Characteristics: SE vs E.

Wages. WSE < WE. And wage growth lower for SE than for E.

Issue of self-selection (panel) for wage levelsWage 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 weekJob 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 = 5.30Self-Employed = 5.08T-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)

UK figures in 2014

Two-thirds of self-employed workers have no pensions

Job Characteristics: SE vs E.US Failure Rates for Start-ups

1 year 25%

2 years 36%

3 years 44%

4 years 50%

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8 years 66%

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:

Positive Negative Competent Impatient Tired

ActivitiesIntimate relations 5.1 0.36 4.57 0.74 3.09 0.2 0.11

Socializing 4.59 0.57 4.32 1.2 2.33 2.3 0.65

Relaxing 4.42 0.51 4.05 0.84 3.44 2.2 0.77

Pray/worship/meditate 4.35 0.59 4.45 1.04 2.95 0.4 0.23

Eating 4.34 0.59 4.12 0.95 2.55 2.2 0.94

Exercising 4.31 0.5 4.26 1.58 2.42 0.2 0.16

Watching TV 4.19 0.58 3.95 1.02 3.54 2.2 0.75

Shopping 3.95 0.74 4.26 2.08 2.66 0.4 0.3

Preparing food 3.93 0.69 4.2 1.54 3.11 1.1 0.62

On the phone 3.92 0.85 4.35 1.92 2.92 2.5 0.61

Napping 3.87 0.6 3.26 0.91 4.3 0.9 0.43

Taking care of my children 3.86 0.91 4.19 1.95 3.56 1.1 0.36

Computer/e-mail/Internet 3.81 0.8 4.57 1.93 2.62 1.9 0.47

Housework 3.73 0.77 4.23 2.11 3.4 1.1 0.49

Working 3.62 0.97 4.45 2.7 2.42 6.9 1

Commuting 3.45 0.89 4.09 2.6 2.75 1.6 0.87

Interaction partners

Friends 4.36 0.67 4.37 1.61 2.59 2.6 0.65

Relatives 4.17 0.8 4.17 1.7 3.06 1 0.38

Spouse/SO 4.11 0.79 4.1 1.53 3.46 2.7 0.62

Children 4.04 0.75 4.13 1.65 3.4 2.3 0.53

Clients/customers 3.79 0.95 4.65 2.59 2.33 4.5 0.74

Co-workers 3.76 0.92 4.43 2.44 2.35 5.7 0.93

Boss 3.52 1.09 4.48 2.82 2.44 2.4 0.52

Alone 3.41 0.69 3.76 1.73 3.12 3.4 0.9

Duration-weighted mean 3.89 0.84 4.31 2.09 2.9

Life Experience: The Day Reconstruction Method". Science, 3 December 2004, 1776-1780.Source: Kahneman, D., Krueger, A., Schkade, D., Schwarz, N., & Stone, A. (2004). "A Survey Method for Characterizing Daily

% time > 0 97% 66% 90% 59% 76%

Mean affect rating Mean hours/day

Proportion of sample reporting

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?”

1989 1997 2005 1989 1997 2005

West Germany 11.0% 11.9% 10.4% 51.4% 61.7% 44.3%Great Britain 11.7% 15.2% 12.9% 49.6% 46.2% 48.7%USA 12.1% 13.4% 13.3% 63.5% 72.3% 64.4%Hungary 5.9% 14.5% 9.0% 42.2% 58.8% 39.1%Norway 5.1% 9.8% 10.9% 26.6% 27.5% 28.4%Sweden 10.7% 10.3% 38.0% 31.8%Czech Republic 10.6% 14.9% 42.8% 30.7%New Zealand 9.2% 15.1% 63.4% 55.0%Canada 15.2% 8.6% 58.7% 55.6%Japan 16.8% 11.4% 42.7% 33.4%Spain 3.4% 14.3% 42.9% 33.9%France 8.9% 8.4% 42.7% 40.6%Portugal 23.6% 14.1% 76.3% 51.8%Denmark 6.5% 8.5% 26.1% 28.4%Switzerland 12.1% 10.1% 65.6% 47.2%

Percentage of Working who are Self-Employed

Percentage of Working who Prefer Self-Employment to Employment

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: ( )

where: θ is entrepreneurial ability (known when making choice)k is capital necessary to start a businessα is returns to scale on capital:

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.

y k

(0,1)

Evans and Jovanovic (1989)

Total entrepreneurial income:

where: z is initial wealth

Constraint:

• Firms can at most borrow λ times their initial wealth to fund their capital project.

Note: Borrowing rate = lending rate = r (same for everyone).

( )y r z k

0 (where 1)k z

Choice of Optimal Entrepreneurial Capital Stock

[0, ]

1

1/(1 )

1

max [ ( )]

. . . : 0

Implication, entrepreneur is unconstrained when:

( )

k zk r z k

F O C k r

kr

rz

Finish Solving The Model: Part 1

Entrepreneurial Income as a function of constrained/unconstrained k.

In the first case, you can borrow all that you want.

In the second case you are credit-constrained (would like to borrow more).

Finish Solving the Model: Part 2Compare Entrepreneurial Earnings to Wages

1 1

1 1

max[ ( )]

Unconstrained:

(1 ) ( )

Constrained:

max ( ) , ( ) ( )

k r z k w rz

r rw z

rz w z r z

Implication of the Model:Probability of Entrepreneurship Increasing in Wealth

This should be (λz)

Evans and Jovanovic Conclusions

• Richer households are less bound by liquidity constraints and as a resultare more likely to enter entrepreneurship.

• Should see a positive relationship between initial wealth and entry intosmall 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

• We do indeed see that the SE are richer than are the employed, which is consistent with the above analysis.

• But it is also consistent with the expected returns to SE being higher than the returns to employment (so that SE leads to wealth, instead of wealth leading to SE).

• We have a potential endogeneity problem…• So we instrument!• Look for exogenous movements in wealth.

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 self-

employed• 33% of those with an inheritance of £50K+ were self-employed

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 (1994-2001), 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 > UA

sort 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 SE CoefficientGeneral 0Car Driving 0Financial Matters +veCareer +veHealth 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 self-employment 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) VSE has been falling because tastes have changed: increasing taste for leisure (SE hours higher) or increasing taste for income (SE income lower).

4) Capital constraints have increased, and are particularly large in France.

5) 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….

6) 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.

There are many other potential explanations of the choice of SE

Particularly across country, work (Torrini, Labour Economics, 2005) has emphasised the role of

Trust/Social capital The Public Sector (crowds out self-

employment? Employer of the last resort?) Corruption (potential for tax evasion?)

Factoids

i) As individuals become richer they are less capital-constrained and so P(SE) rises.

ii) Poor countries have more SE than do rich countries; as GDP rises P(SE) falls.

A sort of SE Easterlin Paradox?

The answer, as is often the case, is in an omitted variable.

As countries grow over time, something else changes which affects P(SE).

i) Corruption fallsii) The public sector becomes largeriii) Trust falls (?)iv) Risk-aversion rises (?)v) The quantity and quality of paid employment

in general rises (as in Bianchi, 2012).

I wonder which of these is the real culprit…

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 self-employed. 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 profitii) SE non-pecuniary return (value of autonomy)iii) Employed wages.

Financial development affects all three, especially in developing countries).

Sample Full Low FD High FD Low FD High FD Low FD High FD(1) (2) (3) (4) (5) (6) (7)

FD*SE 0.3091** 0.8217** 0.0747 0.9113** -0.0249 0.4510 0.1818(0.1390) (0.3476) (0.2238) (0.3576) (0.2366) (0.3431) (0.2117)

GDP*SE 0.0101** 0.0120 0.0063 0.0153* 0.0090 0.0136 0.0124***(0.0047) (0.0074) (0.0056) (0.0079) (0.0058) (0.0087) (0.0045)

Income 0.1142*** 0.0944***(0.0135) (0.0145)

Independence 0.3287*** 0.3513***(0.0123) (0.0124)

SE 0.0426 -0.1703 0.3164 -0.2866** 0.3645* -0.7240*** -0.6136***(0.0916) (0.1370) (0.2067) (0.1347) (0.1858) (0.1442) (0.1873)

Female 0.0077 0.0262 -0.0126 0.0325 -0.0170 0.1444*** 0.0772**(0.0258) (0.0389) (0.0293) (0.0433) (0.0292) (0.0336) (0.0308)

Age 0.0026 0.0080 -0.0026 0.0056 -0.0037 -0.0155** -0.0241***(0.0044) (0.0072) (0.0061) (0.0082) (0.0061) (0.0062) (0.0067)

Age-sq 0.0002*** 0.0001 0.0002*** 0.0001 0.0002*** 0.0003*** 0.0004***(0.0000) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001)

Married 0.1905*** 0.1951*** 0.1848*** 0.1175** 0.1012** 0.1409*** 0.1070***(0.0283) (0.0383) (0.0371) (0.0454) (0.0414) (0.0331) (0.0360)

Education 0.0312*** 0.0392*** 0.0238** 0.0198* 0.0036 -0.0179** -0.0181**(0.0072) (0.0106) (0.0104) (0.0099) (0.0083) (0.0087) (0.0079)

Country*Year YES YES YES YES YES YES YESFixed Effects

Observations 45996 23359 22637 20526 18976 23107 22519R-squared 0.08 0.09 0.07 0.10 0.08 0.23 0.24

Financial Development and Job SatisfactionDependent Variable: Job Satisfaction

Another factoid: Self-employment is more satisfactory than employment… and becoming more so

1989-2005 1989-2005 1997-2005 1997-2005 Self-Employment 0.327** 0.377** 0.353** 0.302** (0.034) (0.062) (0.023) (0.032) Self-Employment*1997 -0.076 (0.081) Self-Employment*2005 -0.061 0.108* (0.085) (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 self-employed; and

iii) The satisfaction “premium” from self-employment is on the rise