Global Workshop on Freelancing & Self-Employment Research

Global Workshop on Freelancing &Self-Employment Research

26-27 April 2018Grange City Hotel, London

www.crse.co.uk | [email protected]

Paths to self-employment: Towards the crucial variable of intentions

Zulaicha Parastuty, Dieter Bögenhold

Alpen-Adria-Universität Klagenfurt, Austria

Background & Research Questions• Comparing different self-employment ratios in different countries shows

remarkable differences, e.g. US 6.5, OECD 15.8, EU28 16.5, Greece 35.2

• Asking for those differences links to different sources of economic power and to further determinants. GEM differentiates between entrepreneurship by necessity and opportunity driven entrepreneurship.

• Too less is asked for an systematic understanding of social psychology in relation to diverse institutional factors across cultures

• Our research is about those “mental dispositions” towards self-employment. This is a research tradition which goes back already to J.M. Keynes and his “animal spirits” as non-economic factors to explain economic dynamics.

Global Workshop on Freelancing & Self-Employment Research｜26-27 April 2018｜Grange City Hotel, London

Data & Variables• Dataset of AMWAY Global Entrepreneurship Research Consortium: Amway, GfK

and TU Munich

• Aim is to provide a picture of how people think of entrepreneurship and self-employment across the globe

• Surveys are conducted from 2010 to 2017 in 45 countries, more than 50.000 respondents

• Data is highly restricted (currently we have the dataset of Austria, UK, US, Italy and Brazil)


Variables

The Amway Entrepreneurial Spirit Index (AESI) aims to capture three key motivational determinants that strengthen the entrepreneurial potential. The three sub-dimensions:

(1.) Desirability: desire to create a start-up,

(2.) Feasibility: idea that chances to create and manage a start-up are realistic,

(3.) Stability: willingness to defend the implementation of a start-up even against a hostile environment of family and friends.

AESI Score may range from 0 to 100.

Key Findings - 1

2926 28

3539

32

46

38

5457

60

4641 41

36

45

56

66

5350

42 4137

4847 49

43

50

0

10

20

30

40

50

60

70

AESI Desirability Feasibility Stability

AESI INDEX 2017Austria UK USA Italy Brasil EU Worldwide

Key Findings - 2

0

10

20

30

40

50

60

70

Women Men Under 35 years 35-49 years Over 50 years With degree Without degree

AESI INDEX 2017Austria UK USA Italy Brasil

Research Implications

• Our academic knowledge how to explain differences in the AESI value is very limited. We don’t have an appropriate tool to deal satisfyingly with divergences which are enormously (100 % or more).

• Which are cultural, legal or economic barriers to prevent people of being more oriented towards entrepreneurship or – in other words –why and how do other countries do better?

• How can we explain the differences in social and economic attitude and behavior which are visible for different socioeconomic variables ? Why are the scores for women mostly lower than for men ?


Research Implications

• The current research findings are just descriptive and they provide first inspections and ideas. Further research must go on to multivariate modelling

• Limitation: we do not have access for the whole dataset, we do not build the dataset. Dataset is highly restricted


Practical Policy Implications• Thinking in terms of policy implications, how can the obvious gender

gap be closed ? E.g. being self-employed is for 31 % men in Austria a positive idea, but only 21 % of Austrian women tend to desire a position in self-employment. The same gender gap can be found regarding the items if a start-up is practically manageable and if a potential start-up can be defended against a hostile social environment of friends or family who are not supportive of this.

• Is the common conclusion that a higher AESI score is generally better than a lower one the politically right assumption ? Or do lower scores sometimes reflect some advantages or strengths in economic behavior of people ?


Are Self-employed Individuals With and Without Employees Driven by the Same Factors?

Empirical Findings from Europe

Ondřej Dvouletý

Department of Entrepreneurship

University of Economics, Prague

[email protected]

This work was supported by Internal Grant Agency of Faculty of Business Administration, University of Economics in Prague, under no.: IP300040.

Introduction

• Entrepreneurship as a field is still growing and that means, there are still many research challenges that are important for both, policymakers and researchers

(Dale, 2015; Parker, 2009).

• Recent empirical studies show, that it is important to distinguish between the various forms of entrepreneurial activity (e. g. Dilli et al., 2018; Jansen, 2018; Van Stel et al., 2014; Stam and Van Stel, 2011)

• In the economy, there are self-employed/entrepreneurs with and without employees who might have different motivations and goals.


Research Gap

• Moreover, an ongoing research on freelancing and self-employment finds that not all entrepreneurs and self-employed want to hire employees (Burke, 2015a; 2015b; Bögenhold and Klinglmair, 2016; Stanworth and Stanworth, 1995)

• Thus, it is very relevant to study factors that affect the decision of own-account workers to hire employees (Petrescu, 2016; Millán et al., 2014a; 2014b; Cowling et al., 2004).


Purpose of the Study and Data

• We aim for diving more into individual characteristics determining the choice of being an employer.

• We utilize three waves of European Survey on Working Conditions (2005, 2010 and 2015); N=105,702

• According to survey data (weighted), the rates of self-employed are in Europe at around 17% of the economically active population (out of 12% of solo-entrepreneurs/own-account workers and 5% of entrepreneurs having employees).

• We aim to identify differences between those self-employed, havingemployees and those, without them.


Comparisons

• Those having employees more likely enjoy being their own bosses, compared to those without employees, however, this relationship is relatively weak (Chi-Square´s p-value < 0.000; Cramer´s V = 0.08).

• There are also significant occupational differences between both groups(ISCO 1 Classification, Chi-Square´s p-value < 0.000; Cramer´s V = 0.30).)

• Most of the self-employed with employees describe themselves as managers (41%)contrary to only 16% of self-employed without employees.

• It is worth mentioning, that solo-self-employed are represented more in the following three occupational groups: Technicians and Associate Professionals; Skilled Agricultural Forestry and Fishery Workers and Elementary Occupations.


Empirical Approach

• We combine two empirical approaches based on estimation of logistic regressions(Likelihood of Being Self-employed with/without Employees):

1) In the first two econometric models (Models 1 and 2), we separately compare Self-employed without Employees and Self-employed with Employees with those being regularly employed (Employed).

2) In the third model (Model 3), we just work with a sample of self-employed only, and we estimate the individual likelihood of being Self-employed with Employees.



Model Model (1) Model (2) Model (3)

Independent/Dependent Variables Self-employed without

Employees

Self-employed with

Employees

Self-employed with

Employees

Age -0.0324***

(0.00482)

0.0636***

(0.00861)

0.0877***

(0.00981)

Age Squared 0.000768***

(0.0000543)

-0.000339***

(0.0000990)

-0.000983***

(0.000106)

Female -0.381***

(0.0211)

-0.897***

(0.0336)

-0.513***

(0.0393)

Pre-Primary education (.) (.) (.)

Primary education or first stage of basic education -0.209*

(0.0922)

0.0525

(0.208)

0.245

(0.213)

Lower secondary or second stage of basic education -0.622***

(0.0917)

0.0434

(0.205)

0.685**

(0.212)

(Upper) secondary education -0.917***

(0.0910)

0.103

(0.203)

1.016***

(0.211)

Post-secondary non-tertiary education -0.865***

(0.0987)

0.289

(0.209)

1.138***

(0.221)

First stage of tertiary education -1.125***

(0.0920)

0.227

(0.203)

1.335***

(0.212)

Second stage of tertiary education -0.737***

(0.131)

0.840***

(0.225)

1.598***

(0.244)

Migrated -0.119**

(0.0436)

-0.193**

(0.0613)

-0.154*

(0.0754)

Living Alone 0.0505

(0.0308)

-0.332***

(0.0504)

-0.446***

(0.0598)

Constant -1.343***

(0.143)

-4.623***

(0.283)

-3.186***

(0.316)

Year Dummies Yes Yes Yes

Country Dummies Yes Yes Yes

Sample Description Employed & Self-

employed without

Employees

Employed & Self-

employed with

Employees

Self-employed

Observations 94,913 87,416 16,913

Pseudo R2 0.119 0.069 0.062

AIC 64242.9 34240.1 18855.5

BIC 64697.0 34690.3 19226.8

Results I

Estimated Robust SE Logistic Regressions with Country and Year Dummies


Results II

Estimated Robust SE Logistic Regressions with Country and Year Dummies

Model Model (1) Model (2) Model (3)

Independent/Dependent VariablesSelf-employed

without Employees

Self-employed with

Employees

Self-employed with

Employees

Age -*** +*** +***

Age Squared +*** -*** -***

Female -*** -*** -***

Pre-Primary education (.) (.) (.)Primary education or first stage of basic

education-* + +

Lower secondary or second stage of basic

education-*** + +**

(Upper) secondary education -*** + +***

Post-secondary non-tertiary education -*** + +***

First stage of tertiary education -*** + +***

Second stage of tertiary education -*** +*** +***

Migrated -** -** -*

Living Alone + -*** -***

Sample Description

Employed & Self-

employed without

Employees

Employed & Self-

employed with

Employees

Self-employed

Conclusions

• For the group of self-employed with employees, the age indicated the traditional inverted U shape, however for the group of those without employees, the results showed the reversed pattern.

• Job creation is positively associated with age that could approximate accumulation of human, financial and social capital. This also supported by the positive relationship between the employment decision and educational attainment (Congregado et al. 2010; Millán et al., 2014a; 2014b).

• Self-employed with a migration background are less likely to hire employees (necessityentrepreneurs/refugee effect, e. g. Mühlböck et al., 2017; Dvouletý and Lukeš, 2016).

• Living alone is less likely to be associated having employees. The partner living in a common household may serve as a source of support (Simoes et al., 2016).


Implications and Future Research

• Presented study supports the argument that self-employed individuals withand without employees are “different animals“.

• If the policymakers aim to support high-growth entrepreneurship, mostly contributing the growth of the economy, they need to better understand characteristics of current and future employers, and perhaps consider these during the application process for receiving public grants and other support from entrepreneurship policies aiming to foster growth and competitiveness of the economy.

• We highly recommend future scholars to study also the role of psychological, intergenerational transmissions and economic circumstances in the decisionto become an employer.


Takeaway

Main findings

• Presented study supports the argument that self-employed individuals withand without employees are “different animals“ and they should beapproached differently by both researchers and policymakers.

Implications for self-employed people

• Those self-employed aiming to become future employers might considerinvestments into their human capital (e. g. tertiary education).

Implications for policymakers

• High-growth entrepreneurship policies’ scenarios and application procedures should take into account characteristics of current employers (e. g. tertiaryeducation).


The new dynamics of Self-Employment: labour market performance vs.

business confidence indexAna Rodríguez-Santiago

Antonio A. Golpe

Emilio Congregado

(Department of Economics, University of Huelva, Spain)

Add your own university/company logo

logo here

Introduction (i)• Governments devise portfolios of policies to promote self-employment as a way

to combat self-employment in recessions (these interventions often imposesizeable costs on the taxpayer).

• Turning unemployment into self-employment can be considered as a temporarystrategy.

• In general, entrepreneurship/self-employment could evolve either pro- or anti-cyclically, depending on the balance of forces at work in the private sector of theeconomy (Rampini, 2004), but we expect different movements in expansions andrecessions.

• The rationale is summarized in the recession-push and prosperity-pull hypotheses


Introduction (ii)• The ‘recession-push’ theory supports the idea that unemployment reduces the

opportunities of gaining paid-employment and the expected gains from jobsearch, which “pushes” people into self-employment (Rissman, 2003).

• By contrast the ‘ prosperity-pull ’ hypothesis represents an alternativemechanism associated to the so-called opportunity-driven entrepreneurs, thoseself-employees whose evolution is pro-cyclical.

The emergence of necessity-driven entrepreneurs associated to the labor marketsituation. The development of the opportunity-driven entrepreneurs figuresassociated to the opportunities.


Introduction (iii)• After the Great Recession: the fall in unemployment rates would be accompanied

by a fall in necessity-driven entrepreneurship –marginal entrepreneurs switch topaid-employment– and maybe by an increase in the opportunity-driven self-employment figures. The net effect could be both, positive and negative.

• UK self-employment rate figures after the Great Recession show persistence.Self-employment rate numbers are similar to the rates before the currentrecovery phase.

• Possible explanations: i) Huge growth of opportunity-driven entrepreneurs, ii) Survival of necessity entrepreneurs because the “new” full employment is now operating differently (i.e. emergences of precariat forms of SE).


Introduction (iv): Aim• We want to explore what kind of self-employment is behind the recent evolution

of self-employment figures and check whether the self-employed sector isresponding in the same way as the British entrepreneurship did in previouseconomic recovery episodes.

• In order to do so we should apply time series techniques for ckecking the macro-dynamics of opportunity and necessity self-employment during the businesscycle.


Introduction (v): Problem and solution• Operational definitions of opportunity versus necessity entrepreneurship using

readily available nationally representative data (Fairlie & Fossen, 2017). Thisoption, however, presents serious problems.

• To circumvent these problems in measuring we avoid the use of these twocomponents of self-employment time series in order to disentangle therelationship, separating the evolution of self-employment into two relationships:

➢ One related to the labour market performance –as the push hypothesesstate–.

➢ A second one depending on the opportunities for profit.


Introduction (vi): Outline• In particular, and by suing Business Confidence Index (BCI) and the

unemployment rate (U) as indicators we provide evidence on:

➢ Turning points dating of Self-Employment rate time series to establish a Self-Employment cycle.

➢ An analysis of the synchronization between the self-employment cycle and the cycles of unemployment and business confidence.

➢ A causality analysis between these set of variables.


Data• Seasonally adjusted monthly data (1992:4 – 2017:12) for the UK on:

• Self-Employment rate: defined as the share of employed people that is self-employed: Number of self-employed/All in employment. (Source: LabourForce Survey, UK)

• Business Confidence Index (Source: OECD)

• Unemployment rate (Source: ONS, UK)

• UK suitable case of study: after the Great Recession, the UK is close tofull employment but self-employment rates remains at the levelsreached during the crisis.


Econometric Strategy• Modification of the Bry-Boschan Business Cycle Dating Algorithm to detect and

date turning points of the self-employment/unemployment and business indexcycles. Filtered probabilities of recession.

• Analysis of causality (non-linear) applied to the relationships between self-employment, unemployment and business confidence index (i.e. SE-BCI and SE-U) as a way to explore the prevalence of opportunity based entrepreneurship ornecessity-driven entrepreneurship depending on the business cycle phase.Assymmetric causality analysis due to Hatemi-J (2012).



11

12

12

13

13

14

14

15

15

16

19

92

-04

19

92

-11

19

93

-06

19

94

-01

19

94

-08

19

95

-03

19

95

-10

19

96

-05

19

96

-12

19

97

-07

19

98

-02

19

98

-09

19

99

-04

199

9-1

1

20

00

-06

20

01

-01

20

01

-08

20

02

-03

20

02

-10

20

03

-05

20

03

-12

20

04

-07

20

05

-02

20

05

-09

20

06

-04

20

06

-11

20

07

-06

20

08

-01

20

08

-08

20

09

-03

20

09

-10

201

0-0

5

20

10

-12

20

11

-07

20

12

-02

20

12

-09

20

13

-04

20

13

-11

20

14

-06

20

15

-01

20

15

-08

20

16

-03

20

16

-10

20

17

-05

20

17

-12

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

TURNING POINTS DATING OF SELF -EMPLOYMENT RATESELF-EMPLOYMENT RATE

UK 1992-2018

Self-employment dating Self-employment rate (LHS)


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

19

92-0

6

19

93-0

1

19

93-0

8

19

94-0

3

19

94-1

0

19

95-0

5

19

95-1

2

19

96-0

7

19

97-0

2

19

97-0

9

19

98-0

4

19

98-1

1

19

99-0

6

20

00-0

1

20

00-0

8

20

01-0

3

20

01-1

0

20

02-0

5

20

02-1

2

20

03-0

7

20

04-0

2

20

04-0

9

20

05-0

4

20

05-1

1

20

06-0

6

20

07-0

1

20

07-0

8

20

08-0

3

20

08-1

0

20

09-0

5

20

09-1

2

20

10-0

7

20

11-0

2

20

11-0

9

20

12-0

4

20

12-1

1

20

13-0

6

20

14-0

1

20

14-0

8

20

15-0

3

20

15-1

0

20

16-0

5

20

16-1

2

20

17-0

7

TURNING POINTS DATING OF UNEMPLOYMENT RATEFILTERED PROBABILITIES OF RECESSION OF SELF -EMPLOYMENT

UK 1992-2018

Unemployment dating Filtered probababilities of recession of Self-employment rate


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1992

-04

1992

-11

1993

-06

1994

-01

1994

-08

1995

-03

1995

-10

199

6-0

5

1996

-12

1997

-07

1998

-02

1998

-09

1999

-04

1999

-11

2000

-06

2001

-01

2001

-08

2002

-03

200

2-1

0

2003

-05

2003

-12

2004

-07

2005

-02

2005

-09

2006

-04

2006

-11

2007

-06

2008

-01

2008

-08

2009

-03

2009

-10

2010

-05

2010

-12

2011

-07

2012

-02

2012

-09

2013

-04

2013

-11

201

4-0

6

2015

-01

2015

-08

2016

-03

2016

-10

2017

-05

2017

-12

TURNING POINTS DATING OF THE BUSINESS CONFIDENCE INDEXFILTERED PROBABILITIES OF RECESSION OF SELF -EMPLOYMENT

UK 1992-2018

BCI dating Filtered probabilities of recession of the Self-employment rate (RHS)

Causality analysis (Non-linear) Self-employment-Unemployment


Causality analysis (Non-linear) Self-employment-Business Confidence Index


Main Results (i)• Causality:

• Labour Market influences

- We find evidence of the recession-push effect.

• Economic Climate

• A positive shock in the business confidence leads an increase in self-employment rates

• On the other hand, both positive and negative shocks into self-employment can be behind to the deterioration of the economic climate.


Main Results (ii)• We provide a turning point dating for the three time series

considered. The comparison between these turning point dating andthe probabilities of recession allow us to establish the periods inwhich recession-push and prosperity-pull prevailed in the last threedecades.

• In the current phase of recovery, the evolution of self-employmentseems to show a different development.


Avenues for further research• In a nutshell, the analysis provide new stylised facts and revisited

some relationships, but leaves some questions open:• what are the reasons why self-employment is higher and more persistent

than before;

• to what extent new economic trends, such as the development of the GIGsector, affect to the relative labour market performance and to the way inwhich economic agents take their occupational decisions.

• A micro-look to the determinants of the transitions from and intoself-employment before and after the Great Recession may shed lightto some of these questions.


Student employment and entrepreneurship

Raquel Justo*, Adrián L. Mérida✢

* Department of Economics, University of Huelva, [email protected].

✢Department of Innovation and Organizational Economics, Copenhagen Business School, [email protected].


Which types of individuals are more likely to engage

and remain in entrepreneurial activities?

Motivation

Entrepreneurship is crucial for economic progress

(e.g. Audretsch and Keilbach, 2004)

We focus on university students, given their high entrepreneurialpotential (e.g. Levine and Rubinstein, 2017)

New companies play an important role in economicgrowth (e.g. Davidsson et al., 1994; Reynolds 1994).

Student employment is a common phenomenon (Darolia, 2014; Scott-Clayton, 2012;Orr et al., 2011) that has captured the interest of researchers and policy makers.

Research question:

How does working while studying affect the entrepreneurial intentions of university graduates?


Literature review (I)

Student employment and entrepreneurial intentions

Financial limitations are not necessarily the most important trigger to seek employment while studying.

Strategic Way

Advantages in the labour market

Human capital Social network perspective Signalling

• Experience• Skills

• Social bonds• Contacts

• Signal potential• Differentiation

Hypothesis 1: Student employment is negatively related to becoming an entrepreneur.


Literature review (II)

Student employment in small firms and entrepreneurial intentions

Workers employed in small firmstend to be more likely to becomeentrepreneurs (e.g. Gompers et al.,2005; Sørensen and Fassioto, 2011)

More accessible in smaller size companies

“Small firm effect” phenomenon

Skills to be an entrepreneur (Shane et al.,

2003):

Leadership

Planning

Decision making

Problem solving

Communication

Conflict Management

Hypothesis 2: Students who work in small firms while studying are more likely to become entrepreneurs

Direct learning from employers

Identifying potential business opportunities


Literature review (III)

According to Lazear’s jack-of-all-trades theory, individuals with varied skills and

knowledge are more likely to become entrepreneurs

Diverse student employment and entrepreneurial intentions

Hypothesis 3: The likelihood that students become entrepreneurs is higher as …

Hypothesis 3c: the number of industries

… where student worked while studying increases

Hypothesis 3b: the number of positions

Hypothesis 3a: the number of firms


Data Description

Data from the official registers of Denmark (IDA)

Detailed: personal attributes, education, labor market, income, parental information...

Final sample:

• 398,308 individuals aged 18-23 when they enrol at university.

• Main independent variable: student employment.

• Main dependent variable: becoming self-employed (while studying or right after graduation).

• 82% had student employment, less than 4% of students become self-employed.


Preliminary Results

Students are more likely to work while studying if they…

• Are females

• Live with their parents

• Live in Copenhagen or Central Denmark

• Study business, health or education

• Are not enrolled in a Master’s program

• Have well educated parents

Determinants of Student Employment

(Results from panel estimations on the probability of student employment).


Preliminary Results

Methodological approach:

• Probability of becoming self-employed in time t as a function

of past student employment.

• Panel OLS regressions (i) without controls, (ii) with controls,

and (iii) with individual fixed effects.

Effects of student employment on entrepreneurship

(I) (II) (III)

β s.e. β s.e. β s.e.

Model A: Dichotomous measure

SE at t-1 (0/1) –0.004 (0.000) *** –0.010 (0.000) *** –0.006 (0.000) ***

Model B: Hours worked per week

Base level: No SE

1 to 9 hours a week –0.009 (0.000) *** –0.009 (0.000) *** –0.004 (0.000) ***

10 to 19 hours a week –0.010 (0.000) *** –0.012 (0.000) *** –0.006 (0.000) ***

20 to 29 hours a week –0.011 (0.002) *** –0.013 (0.001) *** –0.007 (0.000) ***

30+ hours a week –0.013 (0.000) *** –0.012 (0.001) *** –0.009 (0.000) ***

Model C: Continuous measure

Experience gained at t-1 –0.011 (0.000) *** –0.014 (0.001) *** –0.005 (0.001) ***

Cumulative experience –0.000 (0.002) –0.003 (0.000) *** –0.010 (0.000) ***

Model D: Firm size of SE in t-1

No Student Employment 0.006 (0.000) *** 0.007 (0.000) *** 0.005 (0.001) ***

SE in small firm (base level)

SE in medium firm –0.004 (0.000) *** –0.004 (0.000) *** –0.002 (0.001) ***

SE in large firm at t-1 –0.005 (0.000) *** –0.004 (0.000) *** –0.001 (0.000)

Model E: Diverse experience by t-1

a) Number of previous employers 0.003 (0.000) *** 0.001 (0.000) *** 0.004 (0.000) ***

b) Number of previous positions 0.002 (0.000) *** –0.000 (0.001) 0.003 (0.000) ***

c) Number of previous industries 0.003 (0.000) *** 0.003 (0.000) *** 0.003 (0.000) ***

Demographic controls No Yes Yes

Education controls No Yes Yes

Parental background controls No Yes Yes

Region dummies No Yes Yes

Year dummies No Yes Yes

Cohort dummies No Yes Yes

Individual fixed effects No No Yes

Individuals 398,308 398,308 398,308

Observations 1,853,284 1,853,284 1,853,284

Hypothesis 1Student employment negatively affects entrepreneurial intentions

Hypothesis 2Small firm effect mostly reflects selection of entrepreneurial students into small firms.

Hypothesis 3a, 3b, and 3c:Diverse student employment increases entrepreneurial intentions.


Key findings

• Student employment generally decreases entrepreneurial intentions.

• Evidence that student employment increases the opportunity cost of

choosing entrepreneurship over paid-employment.

• Diverse employment leads students to more general skills, and are more

likely to become self-employed.


Key practical implications for organisations and the SE people

Organisations SE People

• Firms may benefit by hiring studentswho worked while studying (they mayhave specific experience + networks).

• Potentially useful for firms to arrangeinternships in order to train and hirestudents in the future.

• Early experiences affect entrepreneurialpropensity.

• Diverse student employment helpsstudents to gather general humancapital, learn about how to run abusiness, and identify opportunity.


Main policy / practice implications

• Promoting student employment will help students tofind better jobs when entering the labour market, whichwill help them become more successful in the future.

• Promoting diverse internships may lead to more start-ups made by university graduates.

• Economic growth

• Lower young unemployment rates

• More competitive labour force


Main takeaway for the press

• Early career decisions and experiences matter.

• Students who work while enrolled at university tend to do itstrategically, in order to differentiate themselves from the otherstudents.

• Students who become entrepreneurs have diverse experiences andprefer to work in smaller firms in order to learn about the process ofrunning a company.


Raquel Justo González

University of Huelva

[email protected]

Thank you for your attention! ☺

The ambiguous meaning of opportunity in the explanation of solo self-employed

intentions to grow

Charlie TCHINDA – University of Namur & UCM-Service d’Études

Marcus DEJARDIN – University of Namur & Université catholique de Louvain

Content

• Interest and background

• Research design and data

• Results

• Discussion


Research interest

• Solo self-employed: an increasing part of the labour force and... a topic attracting a growing interest

• Theory of Planned Behaviour (Ajzen, 1991): Intention is the main predictor/antecedent of entrepreneurial behaviour and/or actions

• Transition from solo self-employment to small size firm ownership

‘’The rate of entrepreneurs who expect extensively to grow their firms in terms of job creation, contributes more to macro-economic growth than entrepreneurial activity in general’’ (Hermans et al., 2015)

• Opportunity motivation and job creation (growth) intentions for solo self-employed

➔ Opportunity driven entrepreneurs contribute more to economic growth (Acs, 2006)


Research design

• Research design• Survey by emailing addressed to south Belgian self-employed workers

• Entrepreneur: Growth oriented and profit seeker

• Ambition/intention to hire more than 3 workers within 3 years

• A modified Wennekers and Thurik (1999) typology/diagram to differentiate entrepreneurs andmanagers among solo self employed

• Entrepreneurs (1) Vs Managers (0) ➔ Simple probit model with HRSE for Solo self-employed

• Explanatory variables• Individual traits, motivation and behaviour

• Firm characteristics (e.g. sector, localization)


Summarizing the model’s findings

• Age 30-54 (+)

• Construction (+)

• Trade sector (+)

• More than 20 years (+)

• Brabant wallon province (+)

• Sciences studies (+)

• Passion (+)

• Previous salary work experience (-)

• Experience in the firm (-)

• Opportunity seekers (-)

• Other sectors (Non profit, and other free professions) (-)

But• Cross sectional and self reported data


Implications

• Opportunity (-): will be developed in the reminding part of the presentation

• Passion (+): • Reinforce the idea that entrepreneurial activity is highly person-specific

• Policy • Include Management (and Entrepreneurship) in Scientific and Engineer cursus

• Not all the self-employed are likely to become (small size) firm owners

➔ Proposition: Policy makers should focus on localizations, sectors and

activities where job creation by solo self employed is more likely to happen


Discussion : the meaning of opportunity (1/2)

• Opportunity : perceived situations in which one expect excess profits compared to those currently available (Shane, 2003)

• Discovered (Kirzner, 1997; 2000)• Previously unknown, overlooked and/or “windfall” exogenous to the entrepreneur

• Previous entrepreneurs’ mistakes and/or market misunderstanding

• Construction (Navis & Ozbek, 2016)• Information search

• Knowledge processing and skills acquisition


Discussion : the meaning of opportunity (2/2)

• Solo self employed perception/intention/action is situation- and person-specific• Preferences, beliefs, motivation and social network

• Regulatory, social, consumers tastes, resources availability and technological changes

➔Market disequilibria, information (asymmetry) and alertness

• Individual cognitive abilities, skills and knowledge

• Nature of the opportunity, individual differences, preferences and regulatory constraints to remain solo self-employed in many activities (freelancers, lawyers,…)

➔ Influence the mode of exploitation of the opportunity (solo versus small business owner)


Figure : Opportunity perception, recognition and selection


Individual Characteristics

- Demographics

- Alertness

- Preferences

- Cognitive

- Skills

- Knowledge

Social network

- Culture

- Religion

- Family

Demand shifters

- Consumers tastes

- Resources

- Technology

Selected, recognized,

perceived opportunities

Context, institutions, regulations and available opportunities

Forward looking

Research

• The meaning of opportunity for solo self-employed• Preference/motivation : orient, select and dynamise behaviour

• Comparison with the intention to grow of small size business owners• Preliminary results

➔ Opportunity shows a positive sign for small business owners intentions to grow


Wellbeing of the (solo) self-employed and (temporary)

employedGiedo Jansen (University of Twente)

With

Isabella Hatak (University of Twente)

Johan Wiklund (Syracuse University)

Old question:

Are the self-employed better off?

New approach

(a) Not place all self-employed on equal footing

(b) Identify relevant comparison groups

(c) Study the causal mechanisms



perceived

uncertainty

(demand)

satisfaction

of need for

autonomy

(control)

permanently

employedtemporary

employed

solo

self-employed

self-employed

employing others

Data and measures

• European Social Survey 2014

• Austria, Germany, Switzerland (N=3,334)

✓Mental Health (depressed, everything an effort, restless sleep, lonely , enjoy life, sadness, being happy, cannot get going)

✓Physical Health (problems with heart, high blood pressure, breathing, back or neck, stomach, skin, headaches, diabetes)

✓Health Deteriorating Behaviour (alcohol consumption, smoking)

✓Health Enhancing Behavior (consumption of fruit and vegetables, physical activities)



Conclusion

✓ wellbeing: self-employed with employees highest. Solo self-employed similar to permanent employees, temporary workers lowest

✓ solo self-employed: relatively low uncertainty, but less able to bear it: uncertainty weakens wellbeing among solo self-employed

✓ social protection of solo self-employed to buffer health risks?

✓ cross-national differences: institutional context matters?

‘Digital divide’ within European entrepreneurs:ICT adoption and use as sources of entrepreneurial heterogeneity and earnings

Serhiy Lyalkov

Andrew Burke

Ana Millán

José M. Millán

André van Stel

International University of Andalusia, Huelva, Spain

Trinity College Dublin, Ireland

Pablo de Olavide University, Sevilla, Spain

University of Huelva, Spain

Trinity College Dublin, Ireland & Kozminski University, Warsaw, Poland


1. Motivation

2. Objectives

3. Background

4. Data and methods

5. Measures

6. Results

7. Conclusions

Outline


Motivation

‒ The worldwide diffusion of the information and communication technology (ICT) has increasedthrough the last decade at breakneck speed

‒ Internet worldwide penetration rate (Internet World Stats, June 2016): 3,675 million

‒ Digital skills are needed

i. to participate fully in society

ii. to improve employment status and income

‒ There are, however, huge inequalities in access and adoption of ICT → ‘Digital Divide’ (DD)

‒ Entrepreneurs are, unfortunately, no exception to these inequalities

‒ In this context, it seems crucial to identify both determinants and effects of DD so that moreaccurate measures to combat DD for entrepreneurs can be designed and implemented


Objectives‒ The first main aim of this work is to analyse the relationship between the existing heterogeneity

within entrepreneurs/self-employed workers and ICT adoption and use

↪ Research question: How does adoption and use of ICT differ between different types of self-employment?

‒ The second main aim of this work is to analyse the relationship between ICT adoption / use and entrepreneurial earnings

↪ Research question: Are earnings from entrepreneurs/self-employed workers who (frequently) use ICT at work substantially higher than earnings from self-employed not using ICT at work?

‒ To provide answers we use:i. the most recent international microdata available

ii. a wide geographical coverage → 35 European countries

iii. 2 proxies to capture heterogeneity within entrepreneurs/self-employed workers based on occupational status andstart-up motive


Background → Incentives & barriers for ICT adoption by entrepreneurs / self-employed workers

INCENTIVES

‒ Increasing productivity and efficiency

‒ Enhance information systems (intra company, suppliers, customers, government)

‒ Market expansion and new opportunities for business → sales and profits increase

Stiroh 2002; Levy and Powel 2003; Ong and Ismail 2008; Alam and Noor 2009; Hashim 2015

BARRIERS

‒ ICT adoption does not fit with the current business (depending on the sector)

‒ Necessity of human resources with high ICT literacy (i.e. workers need ICT skills)

‒ High cost of implementation

‒ Traditional managerial culture

Arendt 2007; Harindranath et al. 2008; Antlova 2009; Choin et al. 2009


Background → Heterogeneity within entrepreneurs / self-employed workers

Several approaches attempted to capture different groups within entrepreneurship / self-employment:

ECONOMISTS → utilise self-employment as a working definition (Parker 2018)

‒ Distinction between self-employed with and without employeesEarle and Sakova 2000; Kuhn 2000; Román et al. 2013; J.M. Millán et al. 2014

– Dependent self-employed workers (i.e., they work mainly or exclusively for a specific client-firm withlimited autonomy and often closely integrated into its organizational structure):

o Widely discussed in international and European political and legal forumsOECD 2000, 2014; Supiot 2001; EIRO 2002; ILO 2003; Sciarra 2005; EU Commission 2006; Eichhorst et al. 2013

o But low number of studies (heterogeneous nature of the situations involved, lack of a definition or statistical tool, lack ofreliable data…)

BUSINESS SCHOLARS → entrepreneurship = opportunity recognition (Shane and Venkataraman 2000).

‒ Typical approach to operationalize opportunity and necessity entrepreneurs based on GEM definitionReynolds et al. 2002, 2005


DATA

Waves 5 and 6 of the European Working Conditions Survey– Sample size: about 44,000 workers per wave – Geographic coverage: 35 European countries (between 1,000 and 4,000 workers per country)– Time coverage: 2010 and 2015

SAMPLE

Men and women aged 18 to 65 working at least 15h per week as self-employed → 7,094 obs.

EMPIRICAL FRAMEWORK

– ICT adoption: Discrete choice ordered models (ordered logit models)– Earnings: none observations are zeros (no censored data) → OLS models (under these

circumstances more appropriate than Tobit models)↪ Single-level and multilevel (hierarchical) models

Data and methods


Measures1st main aim ICT adoption and use

DEPENDENT VARIABLE

ICT use frequency at work (1 = never, … , 7 = all of the time)

MAIN INDEPENDENT VARIABLES

– Employment status: 3 dummies1 Self-employed with employees (0-1)2 Independent own-account self-employed (0-1) (ref.)3 Dependent self-employed workers (0-1)

– Reason to start-up: 4 dummies1 Own personal preferences (0-1) (ref.)2 No other alternatives for work (0-1)3 Combination of both (0-1)4 Neither of these reasons

2nd main aim Earnings

DEPENDENT VARIABLE

Net monthly earnings: PPP $ of 2015 in logs

MAIN INDEPENDENT VARIABLE

– ICT use frequency at work7 dummies (ref. never, i.e., no adoption at all)Continuous variable (1 = never, … , 7 = all of the time)

OTHER FOCAL VARIABLES

– Employment status: 3 dummies– Reason to start-up: 4 dummies

CONTROL VARIABLES → 1st and 2nd main aims

– Educational attainment (3 dummies)– Job aspects: Business sector (10 dummies), Tenure (years)– Demographic indicators: gender, immigrant, age, cohabitation status,

children in the household, health, ability to make ends meet (only 1st aim)

– Degree of urbanization (3 dummies)– Countries (≈ structural differences between countries) (28 dummies) – National unemployment rates (≈ business cycle)– 2015 (vs. 2010) (≈ period of growth vs. crisis)


Results → 1st aim ICT adoption and use

ICT LACK OF ADOPTIONo Compared with IOA, the probability of no

adoption (ICT use freq = 1) decreases by 16% for SEwE and increases by 31% for DSEW

o Compared with OPP, the probability of no adoption (ICT use freq = 1) increases by 16% for NEC

ICT USEo Compared with IOA, the probability of intense

use (ICT use freq = 7) increases by 27% for SEwE and decreases by 36% for DSEW

o Compared with OPP, the probability of intense use (ICT use freq = 7) decreases by 23% for NEC

Independent self-employed are more likely to adopt and/use ICT, compared to dependent self-employed


Results → 2nd aim Earnings

EARNINGSo Those self-employed using ICT at work can earn

between 14 and 20% more than those not using ICT at work at all

o Threshold: biggest step is from ‘Never’ to ‘Almost never’

ICT adoption and use increase earnings from self-employment


Further results on job tenure

• We find positive (direct) impact of job tenure on earnings

• However, we also find:

- Negative impact of job tenure on ICT adoption and use

- Positive impact of ICT adoption and use on earnings

Inertia may hamper business performance in the long run when ICT adoption and use is neglected.

This may be particularly problematic for lower educated entrepreneurs as we also find a strong positive impact of education on ICT adoption and use.

} negative (indirect) impact of job

tenure on earnings


Conclusions

‒ We study determinants and effects of ICT use and adoption while controlling for sector ofeconomic activity

‒ We find that independent self-employed (ISE) are more likely to adopt and use ICT, compared todependent self-employed (and those ISE with employees more so than those without)

‒ ICT adoption and use increase earnings from self-employment

‒ Threshold effect: first adoption of ICT most important

‒ We identified a risk of inertia effects (reluctance to adopt ICT) among lower educated, long-tenured business owners

‒ Policy should stimulate business owners to adopt at least a moderate level of ICT

‘Digital divide’ within European entrepreneurs:ICT adoption as a source of entrepreneurial heterogeneity and earnings

Serhiy Lyalkov

Andrew Burke

Ana Millán

José M. Millán

André van Stel

International University of Andalusia, Huelva, Spain

Trinity College Dublin, Ireland

Pablo de Olavide University, Sevilla, Spain

University of Huelva, Spain

Trinity College Dublin, Ireland & Kozminski University, Warsaw, Poland

Digital freelance platforms –entrepreneurial careers and

activities

Dr Darja Reuschke

Prof Stephen Syrett

Dr Markieta Domecka

ERC-StG-2014 639403 WORKANDHOME

Background – what we know

▪ 5-9% of adult Internet users in Europe work on digital platforms weekly (Huws et al., 2016)

▪Understanding of freelance platforms as ‘remote labour platforms’ with ‘remote workers who never meet clients’ (Kuhn and Maleki, 2017)

▪ Labour processes and gig work (Lehdonvirta, 2018; Ettlinger, 2017; Bergvall-

Kareborn and Howcroft, 2014), e.g. MTurk


What we don’t know

• How do people use digital platforms for skilled work to develop their career?

• How are they using these platforms for entrepreneurial activities?

• Where do they seek to get to?


Framework

• Resources and strategies• Networks

• Knowledge and skills

• Financial resources

• Structural and geographical context• Platforms and markets

• Career and life choices• Personal positions in space and time


43 UK-based digital platform freelancers


Platform People Per Hour: 22 (28) Upwork: 15 (21) Freelancer.com: 6 (12)

Gender 19 women and 24 men

Age groups <30: 6 30-39: 20 40-49: 7 50-59: 8 60+: 2

Occupations Writer, translator, proofreaderIT developerDesignerEngineerBusiness and marketing consult, analystPhotographer

Key findings

• Working and networking on and off platforms

• Different routes to entrepreneurial careers and activities1. Freelancer career - with digital platforms as one source of work/clients

2. Resource acquisition and learning on platforms with aim to start business

3. Business and platform freelancing in tandem

4. Platform as a means of existing business to expand client base

• Skills development through diversity on platforms


Key practice implications

• Strategy development how to use platforms for career and business start-up• How to start-off on platforms? Which strategy? Which platforms?

• Strategies to grow• How to build a team?

• How to combine remote working with employment growth?

• Advances of registered business vis-à-vis freelancing

• Mentoring, advice, information


Main takeaway

• Digital platforms used to acquire some resources for entrepreneurial careers and projects• Knowledge of clients, time and task management

• Not financial resources and limitations re networks

• Combination of platform and off-platform projects and networks

• Difference between platforms re markets, skills, types of tasks posted

• Niche markets and freelancers with specialist skills in global markets


Who undertakes meaningful work on digital freelancing platforms?

Ekaterina Nemkova (the University of Nottingham)

Pelin Demirel (the University of Southampton)

Linda Baines (the University of Southampton)

Background

• Meaningful work is emerging as a ‘fundamental human need’

• Platforms promise autonomy, flexibility and personal freedom

• Concerns grow over how platform economy jobs are eroding the meaningfulness of work that freelancers experience• Unfair pay• Intensity of competition• Work under time pressure• ‘Digital Taylorism’


Research question

There is a notion that creative work is ‘protected’ against erosion of meaning in comparison to microwork as the workers usually have higher level of skills and, therefore, are in control. But, are they?

RQ: Which type of freelancers is more or less likely to experiencemeaningful work in the digital freelancing platforms?


Methodology

• 40 in-depth interviews

• 25 Freelancing designers

• 9 Clients

• 3 Platform providers (executive/founder)

• 2 Industry experts


Summary of findings

The majority of freelancers are aware of an increased precarity which detracts meaning from their work

• Constant search for ‘gigs’ as relationships are short-term• Power skewed towards clients• Lack of autonomy to choose projects• Lack of personal growth • Lack of self-worth and appreciation


Summary of findings

Freelancers who are able to deal better with the constraints of the platform employment have a strong Entrepreneurial orientation

• Proactive in their approach to clients and market-oriented (self-positioning as a ‘creative consultant’)

• Risk-taking: looking for challenges and push themselves to experiment

• Thrive on the intensity of competition


Conclusions

• Unless a person has a strong entrepreneurial orientation it could be difficult to have a meaningful job on the platforms

• But, the majority would not necessarily want to – or actually can –become entrepreneurial

Are platforms really only suitable for the ‘select few’? Will more freelancers joining the platforms have to become entrepreneurial? Or will platforms change and become more secure?


WorkerTech

Philip RossFreelancer – Great Digital Company



• Freelancer and technologist

• One of the Founders of the PCG / IPSE in 1999

• First External Affairs Director

• Campaigned on IR35 and Fast Track Visa and Agencies Act 2003

• For Co-operatives (UK) Co-authored their report – ‘Not Alone’

Inspiration for WorkerTech

Need for labour movement of coops, unions and mutuals to come together

7 recommendations and need for WorkerTech catalyst to help organise

• Independence• Self respect• Personal dignity• Control over their

own work and lives

Precariat enjoy none of the benefits won by organised labour in20th century


https://archive.org/stream/in.ernet.dli.2015.86724/2015.86724.The--Industrial-Worker#page/n15/mode/2up/search/personal+dignity

Organising…commercially (co-ops), with bargaining power(unions) and financially (mutuals)

Through using technology to win back• Independence

• Self respect

• Personal dignity

• Control over their own work and lives


• Mid pay , independent, secure

• Low pay , independent, secure

For which segment of the self employed?


Combined have long term commitment to self employment, represents 40%+ of solo self employed and 1.65m workers (CRSE)

Lots of individual pieces of WorkerTech

• APPS to help me find work. Or match up CareWorkers with those in need

• Agency does timesheets, invoices and contracts all on-line

• I get rating points with platforms

• Using an Organise APP we can petition clients to change working conditions

• Agency asks me already if I know someone else

Often related to a single client or single piece of work


Lots of data in different pots

What should it do?FinTechMy background is as a freelance business analyst working on financial services in London. The prevailing buzz there at present is on ‘fintech’. The definition of fintech, according to the HuffPost is

”FinTech companies are businesses that leverage new technology to create new and better financial services for both consumers and businesses”

The idea is that there is currently loads of great technology in existence and the challenge is to link together new systems and platforms. For instance UBER didn’t write all their own tech, but curated bringing good technology together. In the payments arena things have been helped by some EU legislation, for instance the PSD2 directive that details what API calls banks need to enable and what data needs to be shared.

”WorkerTech companies are businesses that leverage new technology to create new and better services for both freelancers and the businesses that use them”


https://www.huffingtonpost.com/entry/what-is-fintech_us_58a20d80e4b0cd37efcfebaa

https://eng.uber.com/tech-stack-part-one/

Earn income

Manage contract…

paid on time

Self employed

life

Organised with

others

App, agency, coopReferring others

Timesheets, invoices, expenses, contracts, fairness,Paid on time,Checked for tax implications,

Sick pay (breadfunds),Insurance, mortgages, holiday, Christmas Party, workstyle benefits, training, know my rights, recognition

Someone in my corner, organise commercially (flash team or popup coop), push for rights, bargaining power


WorkerTech should be NEEDS focused... These are the key needs

Linking software togetherso its works over the long term

FinTech

• OPEN API

• PSD2 and other compliance

• Micro-services

• SAAS / PAAS platforms

API

standalone

CentralAPP

Chain-linked

Common logon

Platform with piping and shared services


Next steps for WorkerTech

• Functional requirements (the needs)• What data needs sharing and with who

keep and care for my data; access my data; let me use my data

• Technical Requirements• Tech standards, open-api’s, microservices

Put in the roads and infrastructure – the data flow – to enable the rest

Engage the community to make this happen.

Co-ops provide sandbox, run groups and administer.... JOIN US!


Thank you

Philip Ross

[email protected]

@Philiprosslgc

mailto:[email protected]

Models of Labour Outsourcing:Explaining How Clients Use Freelancers to Meet

Their Labour Requirements

John Kitching

Marfuga Iskandarova

ersity/companylogohere

Introduction & Aims

• What labour outsourcing models do clients/end-users adopt to meet their labour requirements? :• freelancers only one form of labour outsourcing

• Why do clients outsource labour services, particularly freelancers, in the ways they do?

• Freelancers defined as all ‘self-employed without employees’


Literature

• need to understand the demand-side of the freelance labour market better:• who uses freelance workers, why & under what circumstances?

• permissive regulatory framework – clients can contract with labour as they wish, subject to broad limits

• research gaps:• most studies are of organisational clients; little on personal consumers

• clients might meet their labour needs in a variety of ways; freelancing is only one approach


Analytical Framework• clients have to decide:

• whether/what to outsource – self-provision, employ, externalise?

• whom to outsource to

• 3 different models of labour outsourcing… • ad hoc freelance projects

• strategic freelancing – repeated use; long-term contracts

• supply chain models

• … contingent upon a range of factors:• client type (personal vs organisational); project size & content

• industry norms

• personal & industry networks


Methods & Data

• qualitative interview study:• Two sectors (publishing, architecture)

• 10 clients/end-users – varying in size, based mainly in & close to London

• 25 freelance workers – 60% female, aged 41-85, established freelancers, mix of full-time/part-time hours

• data – interviews fully transcribed (Nvivo 11)


Findings: examples of the 3 types

• Ad hoc project model:• Architecture – most work undertaken for personal clients;

• Publishing – occasional projects for non-publishing corporates, personal, small firm clients

• Strategic freelancing model:• Architecture – additional skilled labour needed for large projects by architectural firms

• Publishing – publishers organise copy-editing & proofreading work on a freelancer-only basis

• Supply chain model:• Publishing – offshoring to publishing management services companies who then subcontract

to freelancers, including UK!


Conclusions & implications

• clients meet their labour requirements to achieve their personal/business objectives in a variety of ways:

• freelancing is only one option

• permissive regulatory framework allows substantial scope for clients to choose

• labour outsourcing models have variable relevance to different sectors due to differences in client type & size, project scale & content, industry norms

• implications for public policy – value chains, productivity, tax revenue


For a Few Hours More (or Less):Preferred and Actual Working Hours

in EuropeMarc Cowling

Brighton Business School

England

Context and Framing the Research

• Although many theories of labour supply assume an element of choice for the individual, actual choices of working hours may be constrained by institutional arrangements such as working time directives, the presence of trades unions, employer specific characteristics, and also by wider economic forces

• We initially examine the determinants of actual hours worked in Europe,

• And then question the extent to which individuals would choose different hours of work in the absence of hours’ constraints


Theoretical Underpinnings: Economics

• Microeconomic theories of labour supply have their foundations in the labour-leisure trade-off which balances the income earned from working more hours for a given wage and the foregone consumption of leisure

• At the individual level, each worker chooses their utility maximising work hours subject to a physical time constraint and an adjustment for non-work income


Labour Supply Curve


A question…..Or three

• Take a minute to consider what factors you think might explain why hours mismatches might occur

• Do you think that the causes (and potentially consequences) of an hours mismatch are different for under-worked individuals compared to their overworked counterparts?

• Finally, make a guess on what percentage of workers in Europe are;- Underworked- Matched- Overworked


Potential Explanations of Hours Mismatches

• employer preferences (Boheim and Taylor, 2003)

• employees relative lack of control (Reynolds, 2003; Golden, 1996)

• the fixed cost component of employing workers and the variable cost component (Nickell, 1978)

• institutional factors, such as the costs of job search to find employment more closely aligned to ones’ individual hours’ preferences (Bretz, Boudreau, and Judge, 1994; Kahn, 2012)

• availability of suitable jobs (Autor, 2010)


Is it all negative?

• EC working time directives (Barnard, Deakin, and Hobbs, 2003), which sets a maximum of 48 hours per week averaged over seventeen weeks

• On the other hand institutional factors may also act to increase the likelihood of a good hours match for individuals who have a preference for working fewer hours than the ‘standard’ working week (Lyness, Gornick, Stone, and Grotto, 2012)

• the tax system has been used:- in the UK (Brewer, Duncan, Shephard, and Suarez, 2006) to increase labour

market participation amongst single parent families, those with eldercare responsibilities, and older people, particularly at the low wage end of the distribution via the provision of in-work tax credits,

- and in Canada, through the National Child Benefit Program, to increase employment of parents on welfare (Milligan and Stablie, 2007).


And what are the implications of hours mismatches?

• Merz (2002), using German socio-economic panel data for the 1980s and 1990s, argued that working hour tension was an important well-being measure

• conflicts with non-work responsibilities and social stress

• extended into the high performance working practice literature (White, Hill, McGovern, Mills & Smeaton, 2003) alongside the working hours –home life relationship

• Lyness et al (2012), in their detailed analysis of the 1997 International Social Survey Programme Work Orientations Survey for 21 countries, take a more nuanced approach to time sovereignty by considering the ability to influence the duration of work (hours), and the timing of the supply of those hours


Preferred Hours Actual Hours

Enhancing Factors

Constraining Factors

Overworked

Matched

Underworked

Influence On

OtherLife

Domains


The data

• The European Working Conditions Survey (EWCS) is conducted by the European Foundation for the Improvement of Living and Working Conditions (EFILWC). The EWCS series began in 1990-1991, and is generally conducted once every five years

• The aim of the EWCS is to provide an overview of the state of working conditions in the EU, to identify major issues and changes affecting the workplace and to contribute to a better monitoring of the quality of work and employment in Europe


Hours and Pay

ISE JC Waged

Hours worked 46.4 49.6 38.1

Hours preferred 39.5 41.2 36.6

Monthly pay (Euro) 974 1,411 1,158


Under-Matched-Over Hours

0

10

20

30

40

50

60

70

ISE JCSE Waged

% o

f w

ork

ers

Over Equal Under


0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00

Turkey

Sweden

Greece

Montenegro

Portugal

Denmark

Albania

Germany

Czech Republic

FYROM

Slovakia

Spain

Finland

Norway

Kosovo

Luxembourg

Cyprus

France

Ireland

United Kingdom

Austria

Netherlands

Italy

Poland

Hungary

Croatia

Belgium

Slovenia

Malta

Latvia

Estonia

Lithuania

Romania

Bulgaria

% of total workforce

Mismatched Hours: Underemployment

0 5 10 15 20 25 30

Latvia

Ireland

Lithuania

Malta

France

Italy

Estonia

Netherlands

Slovenia

Slovakia

Portugal

Romania

Germany

Spain

Hungary

Belgium

FYROM

Croatia

Finland

United Kingdom

Poland

Bulgaria

Albania

Norway

Greece

Cyprus

Denmark

Montenegro

Luxembourg

Sweden

Czech Republic

Austria

Kosovo

Turkey

% of total workforce

Mismatched Hours: Overemployment

10

20

30

40

50

60

Fitt

ed

valu

es

0 50 100 150 200Hours_Worked


And if I could choose my hours…

0

10

20

30

40

50

60

70

80

90

100

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90

Des

ired

Ho

urs

Actual Hours

ISE JC WAGED EQUALITY


Factors Influencing Hours Mismatches

Underworked

• Greece

• Public sector

• Not-for-profit

• Household size ‘∩’

• Job tenure ↑

• Previously Unemployed

Overworked

• Southern Med

• UK

• Male

• Not main income earner

• Lower level occupations

• Primary, Construction, Transport, Real Estate

• Small Business Owners


General Summary

• the average overworked (underworked) person would choose a reduction (increase) in their hours of around 13 hours per week

• the widely accepted ‘standard working week’ of 35-40 hours is the most desirable for under-and-over worked individuals

• recent trends in labour markets such as zero hours contracts, part-time working, and long-hours self-employment are not conducive to individual worker utility maximisation

• Country, occupation, and industry sector are the major influences on overwork

• Hours mismatches can spill-over into all other socio-economic domains and impact on well-being


Key findings, practical and policy implications, and press headlines

• Solo SE work 8 hours per week longer than waged workers

• Their preference is to reduce their hours by 7 per week

• Solo SE are the least likely to work hours that match their preferences

• 40% of Solo SE are over their preferred weekly hours

• Taking into account differences in workers and jobs across Europe, UK workers are amongst the most likely to be overworked

• Overwork is most prevalent in primary industries, construction, transport, and real estate

• Underwork is most prevalent amongst those entering work from unemployment

• These findings suggest that managing work-life balance may be a particular issue for Solo SE and that self-employment does not always equal autonomy


Who stops being solo self-employed and who doesn’t?

Lian Kösters (Researcher @Statistics Netherlands (CBS), PhD @University of Amsterdam, contact: [email protected])

Introduction


Source: EU-LFS

Research questionsWho stops as a solo self-employed and who doesn’t?

• Do solo self-employed who start from a situation of paid employment remain self-employed longer than solo self-employed who start from a situation as a recipient of social benefits?

• Are there differences between economic sectors in the duration of the solo self-employment?

• Are certain demographic groups of solo self-employed more likely to stop (and go into a situation of receiving social benefits)?


DataIntegral data tax authorities (2007-2016*) on reported yearly income

Main socio-economic position (main income), other (secondary) income

- self-employed entrepreneurship (profit);

- owner-manager (wage);

- ‘other’ self-employed (result from other labour);

- no employees;


Definition + operationalizationsolo self-employed

Key findings


Key findings (summary)

• Probability to stop gets smaller with longer duration self-employment

• Starting position important factor in probability to stop

• Different demographic background shows difference in probability to stop

• Stopping probabilities differ per sector

• Solo self-employment as main/secondary income important factor in probability to stop


Important news/practical

implication

Key policy implications


• Not all solo self-employed are equally successfull: important fortarget group policy

• (Growing) group of ‘combiners’ has a higher probability to stop self-employment: what stops them from remaining

• Heterogeneity of the solo self-employed

Future research

• Main/secondary income as starting point of solo self-employment

• Other dimensions of ‘success’ of solo self-employed: incomedevelopment and hiring employees

• Sequence analysis to determine specific careers


Bank lending to solo self-employed consultants

Matthijs den Besten, Montpellier Business School

Aim: Investigate the impact of gender, citizenship and marital status


© SME Finance Forum

• “Access to finance remains a major barrierto entrepreneurs” (EC 2003)

• “Access to finance not as big of a problem for SMEs” (Gangcuangco, 2018)

• “Lack of funding robs UK SMEs of growth opportunities” (Maslen 2018)

• All credit to men? (Marlow & Patton, ETP,2005)

Outline

• Review of literature on Access to Finance & Gender

• Observations on solo self-employed consultants & their characteristics

• Regression results (OLS)

• Implications for policy & further research


Access to Finance - a review of research texts on women’s entrepreneurship (Ahl, 2004)

Women seem to be discriminated against by banks in several studies, but the explanations appear to be mainly structural

• They own the types of businesses that banks associate with higher risks

• The family is hypothesized to be of special significance for women entrepreneurs

• The main finding of difference that is somewhat consistent across the studies is that women’s businesses are concentrated in the retail and service sectors and, because of this, their businesses are smaller than the average male owned business


Access to Finance (continued)

• Female entrepreneurs risk receiving significantly less venture capital (Malmström, Johansson & Wincent, 2017)• associated with lower early business growth (Alsos et al., 2006)

• Black-owned small businesses are about twice as likely to be denied credit (Blanchflower, Levine & Zimmerman, 2003)• greater personal wealth is associated with a lower probability of loan denial

(Cavaluzzo & Wolken, 2005)

• Collateral as a remedy for credit rationing (Steijvers & Voordeckers, 2009)


Method

1. Select companies that have experienced growth in first 3 years

2. Check whether they obtained bank loans in the next 2 years (dependent variable)

3. Look for the impact of features of the owner-manager• Citizenship, partner implication, education/experience

4. Interact with gender

5. Control for personal wealth• Part of personal resources invested to finance creation of enterprise

6. Control for firm size, type of early investments, growth objectives, and existence of prior bank loans.

7. Estimate as linear model


Sample selection from survey of new firms (2002-2007)

3486 successful solo self-employed consultants

Sample description: gender, citizenship & marital status

Results – Ordinary least squares estimation. Dependent variable: got bank loan in period 05-07

Female Owner-Manager Male Owner-Manager

Partner not involved - 0.082** (0.039) - 0.035* (0.021)

Is single - 0.080* (0.047) - 0.054** (0.027)

Is EU citizen 0.031 (0.073) - 0.176*** (0.054)

Is Non-EU citizen - 0.246** (0.117) - 0.015 (0.043)

Has University degree 0.085** (0.041) - 0.028 (0.020)

26-50% personal resources - 0.019 (0.046) - 0.080*** (0.025)

51-75% personal resources - 0.173* (0.090) - 0.029 (0.039)

> 75% personal resources 0.007 (0.031) - 0.110*** (0.018)

Controls Yes Yes

Observations 827 2659

R^2 0.178 0.143

Key Findings wrt bank loans granted

• No direct effect of owner-manager’s gender

• Positive effect of partner involvement

• Effect of citizenship status (French vs. EU vs. non-EU) depends on gender

• Education matters for women but not for men

• Greater commitment of personal wealth associated with lesser likelihood of use of bank loans in firms led by male owner-managers


Interpretations: Lender discrimination, borrower inhibition, or lender-borrower affinity?

• Lender discrimination:• Women required to send stronger signals of trustworthiness than their male peers

• Borrower inhibition:• Solo self-employed consultants of certain socio-economic backgrounds lack

confidence to ask for funds and/or prefer alternatives (Moro et al., 2017; Kwapisz & Hechavarría, 2018)

• Lender-borrower affinity (homophily): • Creditors are more likely to trust people like themselves (Fisman et al., 2017; Beck et

al., 2018)


Implications on the ground

• Borrowers (self-employed people):• Personal characteristics matter; try to match the stereotype

• Lenders (banks):• Borrowers with an atypical profile might present an opportunity

• Recruiters (banks):• Appoint lending officers which match the target clientele


Main policy implication: affirmative action needed• “The central challenge for policy-makers is to identify those `infra-marginal' firms which

are strong enough to survive, but not strong enough to grow.” (Parker, 2009)• Underserved solo solo self-employed consultants should be targeted

• Schemes like the Small Business Loan Guarantee can positively influence the supply of funds (Marlow & Patton, 2005)

• Through preferential treatment of disadvantaged groups

• Support and advice on financial decision making, available for SMEs, are important for them to better manage and to access finance (Han & Benson 2010), but uptake is often low among ethnic minorities (Ram & Smallbone, 2003)

• And provision of tailored business advice


Main Takeaway:

Among solo self-employed consultants some are more equal than others with regards to access to finance


Policy dilemmas regarding the distinctiveness of the solo self-

employed on the labour market - the case of Poland

Prof. Jerzy Cieślik

Director Center for EntrepreneurshipKozminski University, Warsaw, Poland

Poland: Regulatory Environment for Solo Self-Employed

• Study on the regulatory framework on solo self-employed (SS-E) carried out as part of the broader research project

• Sizeable population of SS-E in Poland

• SS-E not organized as a group with distinct economic and social interests

• Dominating Marxist perspective based on the dichotomy: employees vs employers


SS-E: Conflicting regulatory perspectives


Small BusinessPolices

Labour&

Trade Union

Regulations

SS-E

SS-E as entrepreneurs

• New Entrepreneurs’ Law passed March 2018

• Solo business owners – the smallest and weakest segment thus deserving additional startup support

• Non-registered business owner with monthly revenue below 250 Euro

• 6 months grace period on social security contributions

• Registered solos (not employers) having right to indefinitely suspend business activities


SS-E under revised Labour Code (draft)• A person can be engaged by the employer:▪ As employee

▪ As registered solo business owner

▪ Under non-employment contract➢ Available only to professionals with high earnings

➢Not applicable for low-paid workers to protect their rights

• Certain „workers’ privileges” granted to all categories

• False self-employment: Doubtful cases considered as employment relationship


SS-E and trade union membership


Persons engaged in income–generating activity

Persons engaged in income–generating work Employers

Employees

SS–Ewith

established group interests

Eligible for trade union membership

Implications for researchers, SS-E organizations, and policymakers

• The study of Polish regulatory environment has identified a significant knowledge gap among law- and policy-makers:• Lack of knowledge and understanding of the distinctiveness of SS-E

• The prevailing Marxist perspective: employees vs employers

• Resulting in laws and policies incompatible with new ways people engage in economic activity

• Presumably more countries lagging behind the leaders, like UK

• Researchers to be more proactive in bringing the research results to policymakers and engage in policy debates


Implications cont.• Lack of adequate representation of SS-E in the regulatory process

• Effort needed on the part of national and international organizations for SS-E in the leading countries to establish similar organizations in other countries

• Exchange of good practices in shaping effective laws and policies on SS-E as part of inter-governmental co-operation


Discussion

Prof. Jerzy CieślikDirector Center for Entrepreneurship

Kozminski University, Warsaw, Poland

The study has been carried out as part of theresearch project no. 2015/19/B/HS4/00366

Self-employment from Polish and international perspective funded by the National Science Centre of Poland


Seeking a BetteR Regulatory Framework for the Self-

employedPatricia Leighton

An investigation: But why?

• Major changes in global employment relations that challenge so many traditions and practices of ‘the world of work’

• First,’ external changes’ affecting employment, including self-employment-responses spearheaded by OECD and the EU on tax, security, major inequalities, technological change and concerns about even notions of capitalism

• Second, ‘internal changes’ affecting the way work is provided, accessed,and organised, especially through intermediation, adding complexity, disruption(?).Much having relevance for employment identity, classification and protections? Including employment categorisation.

• Why now? How?


CHALLENGES • S

Already global

litigationAlready

regulatory issues

Already many uncertainties

AND QUESTIONS Legal status? Quality? Access to Social Protections(?) but also

Accountability/Insurance/Liability

ONE BASIC ISSUE

• Employment status: categorising those at work….

• Two main categories, but also ‘self-employed workers’

- kind of mixture of employee and self-employed

❑Huge differences in approach to differentiating

- the employee from the self-employed in different

- jurisdictions.

• We need to remember two key issues………

- First, diversity among the apparently self-employed(CRSE)

- Second, to take a ‘lifetime’, sustainable perspectiveGlobal Workshop on Freelancing & Self-Employment Research｜26-27 April 2018｜Grange City Hotel, London

HOW DO WE DECIDE?• Risk


Investment/Risk/Businesss?

(Lack of?

Control/Subordination

Integration (Or not)

Interdependence or independence

AND?

SO………….?• Where to start?

• By using historical, anthropological, sociological, legal data,to

to try to clarify the essential identity of self-employment but

also the ‘employee’. (Does ‘freelance’,’Ipro’,’contractor’ etc

as widely used terms help? )

• To decide whether being self-employed is a business or

type of worker? And whether we have one definition for all

regulatory areas, social protections.

It is suggested that a single robust test is impossible,

WHY NOT HAVE A SINGLE STATUS….one size fits all!


The emergence of a new form of dependent (and precariat) work: the

underemployed self-employmentMaría Isabel de Andrés

(International University of Andalusia, UNIA Spain)

Concepción Román

Emilio Congregado

(Department of Economics, University of Huelva, Spain)

Some stylised facts

• Trends point to underemployment becoming more prevalent after thecrisis (BLS, 2010, ONS, 2017).

• Some countries are close to full employment but accompanied by a‘chronic’ underemployment.

• This phenomenon is a ‘hot policy issue’ for both scholars andpractitioners at this time.


Literature on Underemployment Research• Worker in an activity/job that is inferior by some standard (paid-

employed or self-employed): low quality/substandard jobs• Underutilized, underpaid, overeducated, overskiled, ineadequately employed

• Underemployment research (McKee-Ryan & Harvey, 2011):• Theory: seminal model (Feldman, 1996)

• Propositions

• Empirical literature:• determinants

• outcomes

• However works of underemployment into self-employment are relativelyscarce. Filling this gap, empirically, is the aim of this work.


Literature on Underemployment Research (II)• However the study of underemployment into self-employment is now

important.

• The emergence of Precarious self-employment is a new phenomenonassociated to the development of the GIG sector and to poorly paidand part-time gigs.

• Thus, new forms of segmentation in the labor market are emergingamong workers in full-time jobs and these new forms of precariatself-employment.


The gap• Although a large body of literature explores the determinants of the

probability of becoming self-employed worker, research ondependent self-employment is relatively scarce (see, Román et al,2013 for a survey).

• This paper treats to identify and characterize new forms ofunderemployed self-employment that emerges in parallel to thedevelopment of the GIG sector and the digital economy in aframework in which paid-employees are being substituted by self-employed workers that are underemployed and have precariatworking conditions


Outline• Methodology and Data

• Results

• Conclusions

• Avenues for further research


Methodology & Data (i)• DATA

• Last two waves of the European Working Conditions Survey (EWCS)• Geographic coverage: 35 European countries

Albania, Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia,Finland, France, Former Yugoslav Republic of Macedonia, Germany, Greece, Hungary,Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Montenegro, Netherlands, Norway,Poland, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey,UK

• Time coverage: 2010 and 2015

• SAMPLEMen and women aged 18 to 65 working as :– Self-employed (11,027 ind.)– Paid-employee (64,097 ind.)

• EMPIRICAL FRAMEWORK• Binary discrete choice models (binary logit models)


Methodology (ii)

DEPENDENT VARIABLES

▪ Underemployment – Working hours• Probability of working less hours than desired (0-1)

▪ Underemployment – Payment• Probability of feeling to be paid appropriately (0-1)

▪ Underemployment – Overskilling• Probability of having the skills to cope with more demanding duties (0-1)

▪ Underemployment – Job Dissatisfaction• Probability of being dissatisfied with working conditions (0-1)

▪ Underemployment – Other paid job(s)• Probability of having any other paid job(s) besides the main paid job (0-1)


Methodology (iii)


▪ Employment status – Paid employment (0-1) (ref.)– Self-employment (0-1)

▪ Self-employment heterogeneity– Self employment without employees vs. with employees

• Paid employment (0-1) (ref.)

• Self employment without employees (0-1)

• Self-employment with employees (0-1)

– Dependent self-employment vs. independent self-employment• Paid employment (0-1) (ref.)

• Dependent self employment (0-1)• Those fulfilling at least 2 of these 3 conditions: (i) Not having the authority to hire or dismiss employees; (ii) Getting paid an

agreed fee on a weekly or monthly basis; (iii) Not having more than one client or customer

• Independent self-employment (0-1)


Methodology (iv)


– Necessity self-employment vs. opportunity self-employment (EWCS 2015)• Paid employment (0-1) (ref.)• Necessity self employment (0-1)

Those declaring to became self-employed because they had no other alternative for work• Opportunity self-employment (0-1)

Those declaring to became self-employed through own personal preference– Types of self-employment (EWCS 2015)

• Paid employment (0-1) (ref.)• Sole director of own business (0-1)• A partner in a business or professional practice (0-1)• Working for herself (0-1)• Working as a sub-contractor (0-1)• Doing freelance work (0-1)• Paid a salary or a wage by an agency (0-1)• Other (0-1)


Methodology (v)

CONTROL VARIABLES

• Demographics– Female (0-1)– Age (18-65) and age squared – Household size (0-6)– Immigrant status (0-1)– Health status (1-5)

• Educational attainment – Basic education (0-1) (ref.)– Secondary education (0-1)– Tertiary education (0-1)

• Economic situation– Ability to make ends meet (1-6)

• Job related aspects– Tenure (0-49) and tenure squared– Business sector (17 categories; ref.

Construction)

• Country dummies (35 categories; ref. Spain)

• Time dummies (2 categories; ref. 2010)


Main Results (i) Underemployment-Working hours


Specification I II III IV V

# of observations 68,817 68,645 67,172 36,703 36,850

Log likelihood -24727.8 -24582.9 -24229.7 -12619.8 -12689.0

Predicted probability (y) 0.1091 0.1082 0.1100 0.1012 0.1013

Independent variables (x) Marg. eff. (%) t-stat Marg. eff. (%) t-stat Marg. eff. (%) t-stat Marg. eff. (%) t-stat Marg. eff. (%) t-stat

Main independent variables: Employment status

Paid employment (ref.)

Self-employment 11.71 3.34 ***

Self-employment heterogeneity: without vs. with employees

Self-employment without employees 25.47 6.02 ***

Self-employment with employees -34.79 -6.75 ***

Self-employment heterogeneity: dependent vs. independent

Dependent self-employment 51.66 5.77 ***

Independent self-employment 6.15 1.51

Self-employment heterogeneity: necessity vs. opportunity

Necessity self-employment 44.04 5.52 ***

Opportunity self-employment 6.97 1.09

Self-employment heterogeneity: type of self-employment

Sole director of own business -4.52 -0.58

A partner in a business or professional practice -35.34 -3.01 ***

Working for herself 38.96 5.10 ***

Working as a subcontractor 54.77 1.69 *

Doing freelance work 88.59 4.74 ***

Paid a salary or a wage by an agency 176.42 2.01 **

Other 109.80 2.98 ***

Control variables

Female 11.25 4.89 *** 10.75 4.65 *** 11.16 4.81 *** 16.99 5.20 *** 16.21 4.98 ***

Age -6.72 -10.38 *** -6.58 -10.11 *** -6.65 -10.18 *** -7.01 -7.82 *** -6.78 -7.59 ***

Age squared 0.07 9.33 *** 0.07 9.05 *** 0.07 9.11 *** 0.08 7.06 *** 0.07 6.83 ***

Household size 0.92 1.10 1.16 1.38 1.22 1.45 0.81 0.69 0.77 0.66

Immigrant status 22.31 6.72 *** 22.54 6.76 *** 22.16 6.64 *** 22.81 4.83 *** 22.76 4.83 ***

Health status -3.03 -2.04 ** -2.93 -1.96 ** -2.92 -1.94 * -6.25 -2.96 *** -6.34 -3.02 ***

Secondary education -9.67 -1.88 * -8.79 -1.70 * -10.33 -1.98 ** -23.41 -3.10 *** -23.79 -3.17 ***

Tertiary education -36.78 -7.50 *** -36.09 -7.29 *** -37.37 -7.54 *** -50.82 -7.46 *** -50.81 -7.52 ***

Ability to make ends meet -29.29 -31.91 *** -28.81 -31.18 *** -28.87 -31.14 *** -28.74 -21.81 *** -28.85 -22.02 ***

Tenure -7.15 -18.71 *** -7.13 -18.57 *** -7.17 -18.57 *** -8.10 -15.17 *** -8.01 -15.07 ***

Tenure squared 0.13 11.85 *** 0.14 11.85 *** 0.14 11.92 *** 0.15 9.65 *** 0.15 9.53 ***

Business sector dummies (17 categories; ref. Construction) Yes Yes Yes Yes Yes

Country dummies (35 categories; ref. Spain) Yes Yes Yes Yes Yes

Time dummies ((2 categories; ref. 2010) Yes Yes Yes No No

Main Results (ii) Underemployment-Payment



# of observations 68,817 68,645 67,172 36,703 36,850

Log likelihood -38766.8 -38646.1 -37998.5 -20493.3 -20599.6





Self-employment -4.62 -2.43 **


Self-employment without employees 1.21 0.55



Dependent self-employment -8.88 -2.19 **

Independent self-employment -0.19 -0.09



Opportunity self-employment -18.45 -5.80 ***



A partner in a business or professional practice -17.37 -2.36 **

Working for herself -5.09 -1.41

Working as a subcontractor -2.65 -0.18

Doing freelance work 1.84 0.24

Paid a salary or a wage by an agency 68.77 2.02 **

Other -20.25 -1.56

Control variables

Female 8.91 6.61 *** 8.69 6.43 *** 8.69 6.41 *** 7.16 3.76 *** 7.50 3.98 ***

Age -0.05 -0.12 -0.03 -0.07 -0.10 -0.24 -0.09 -0.16 -0.06 -0.10

Age squared 0.00 -0.52 0.00 -0.55 0.00 -0.37 0.00 -0.33 0.00 -0.41

Household size -0.39 -0.78 -0.32 -0.64 -0.37 -0.74 0.10 0.15 0.11 0.16

Immigrant status 5.06 2.61 *** 4.87 2.50 ** 5.12 2.63 *** 4.20 1.55 4.54 1.68 *

Health status -22.27 -25.17 *** -22.31 -25.15 *** -22.07 -24.78 *** -21.54 -17.28 *** -21.78 -17.54 ***

Secondary education -6.07 -2.07 ** -5.82 -1.97 ** -6.12 -2.05 ** -6.42 -1.43 -6.78 -1.52

Tertiary education -11.65 -3.80 *** -11.31 -3.67 *** -11.66 -3.75 *** -9.55 -2.05 ** -10.00 -2.16 **


Tenure 0.16 0.74 0.18 0.84 0.19 0.87 0.87 2.87 *** 0.84 2.80 ***

Tenure squared 0.01 1.50 0.01 1.37 0.01 1.27 -0.01 -0.95 -0.01 -0.90




Main Results (iii) Underemployment-Overskilling



# of observations 68,817 68,645 67,172 36,703 36,850

Log likelihood -41143.0 -41028.7 -40104.2 -21502.1 -21595.3








Self-employment with employees 4.91 1.59


Dependent self-employment 5.23 1.24

Independent self-employment 7.06 3.24 ***


Necessity self-employment 2.26 0.59

Opportunity self-employment 7.53 2.30 **


Sole director of own business 10.39 2.44 **

A partner in a business or professional practice -16.35 -2.34 **

Working for herself 5.34 1.45

Working as a subcontractor 30.27 1.88 *


Paid a salary or a wage by an agency 29.82 0.94

Other 27.96 1.72 *

Control variables

Female -6.19 -4.75 *** -6.21 -4.75 *** -6.33 -4.79 *** -6.12 -3.31 *** -6.14 -3.33 ***

Age 0.97 2.41 ** 1.01 2.50 ** 1.05 2.57 ** 1.76 3.13 *** 1.70 3.03 ***

Age squared -0.01 -2.02 ** -0.01 -2.11 ** -0.01 -2.17 ** -0.02 -2.93 *** -0.02 -2.84 ***

Household size -1.45 -2.98 *** -1.47 -3.02 *** -1.47 -2.99 *** -1.59 -2.31 ** -1.52 -2.22 **

Immigrant status 6.38 3.39 *** 6.35 3.37 *** 6.30 3.30 *** 7.18 2.68 *** 7.21 2.69 ***

Health status 3.39 3.89 *** 3.42 3.91 *** 3.19 3.61 *** 5.05 4.05 *** 5.12 4.12 ***

Secondary education 4.34 1.45 4.04 1.35 5.04 1.65 * 3.55 0.76 4.26 0.92

Tertiary education 18.86 5.71 *** 18.59 5.61 *** 19.78 5.83 *** 20.11 3.94 *** 21.01 4.14 ***

Ability to make ends meet -2.41 -4.46 *** -2.36 -4.34 *** -2.51 -4.58 *** -1.95 -2.51 ** -1.95 -2.53 **

Tenure -0.40 -1.83 * -0.40 -1.85 * -0.42 -1.93 * -0.52 -1.72 * -0.50 -1.66 *

Tenure squared 0.00 0.34 0.00 0.37 0.00 0.44 0.01 0.83 0.01 0.78




Main Results (iv) Underemployment-Job dissatisfaction



# of observations 68,817 68,645 67,172 36,703 36,850

Log likelihood -27048.4 -26933.5 -26451.4 -13717.9 -13842.2





Self-employment -11.33 -4.06 ***


Self-employment without employees -4.92 -1.52



Dependent self-employment 13.36 1.99 **

Independent self-employment -14.04 -4.45 ***



Opportunity self-employment -46.62 -11.01 ***


Sole director of own business -23.69 -4.03 ***

A partner in a business or professional practice -52.67 -5.80 ***

Working for herself 1.67 0.30

Working as a subcontractor -2.23 -0.10


Paid a salary or a wage by an agency 287.84 3.47 ***

Other 24.07 1.01

Control variables

Female -3.91 -1.86 * -4.30 -2.04 ** -3.87 -1.82 * -8.02 -2.66 *** -7.80 -2.60 ***

Age -0.48 -0.75 -0.39 -0.60 -0.22 -0.34 -0.38 -0.42 -0.16 -0.18

Age squared -0.01 -1.26 -0.01 -1.40 -0.01 -1.69 * -0.01 -0.93 -0.01 -1.19

Household size -2.34 -3.04 *** -2.20 -2.85 *** -2.25 -2.90 *** -2.29 -2.08 ** -2.30 -2.10 **

Immigrant status 19.58 6.05 *** 19.53 6.02 *** 19.25 5.91 *** 11.17 2.52 ** 11.40 2.58 ***

Health status -59.85 -44.70 *** -59.90 -44.58 *** -59.93 -44.29 *** -60.33 -31.57 *** -60.55 -31.92 ***

Secondary education -6.61 -1.57 -6.00 -1.42 -6.35 -1.47 2.53 0.39 0.96 0.15

Tertiary education -10.76 -2.41 ** -10.13 -2.25 ** -10.30 -2.26 ** 2.03 0.28 0.18 0.03


Tenure -0.37 -1.09 -0.39 -1.14 -0.41 -1.19 0.08 0.16 0.09 0.18

Tenure squared -3.91 -1.86 * -4.30 -2.04 ** -3.87 -1.82 * -8.02 -2.66 *** -7.80 -2.60 ***




Main Results (v) Underemployment-Other paid jobs



# of observations 68,817 68,645 67,172 36,683 36,830

Log likelihood -17900.8 -17840.0 -17490.3 -9712.6 -9751.8








Self-employment with employees 2.06 0.27


Dependent self-employment 13.54 1.30

Independent self-employment 19.81 3.58 ***


Necessity self-employment 22.96 2.36 **

Opportunity self-employment 7.78 1.02



A partner in a business or professional practice -1.82 -0.11

Working for herself 17.91 1.98 **

Working as a subcontractor 55.79 1.36

Doing freelance work 75.88 3.45 ***

Paid a salary or a wage by an agency -3.88 -0.06

Other 65.57 1.47

Control variables

Female -35.58 -11.92 *** -35.75 -11.95 *** -35.79 -11.83 *** -36.70 -9.10 *** -37.05 -9.21 ***

Age -0.29 -0.32 -0.25 -0.28 -0.06 -0.07 -1.27 -1.09 -1.09 -0.94

Age squared 0.00 -0.43 -0.01 -0.50 -0.01 -0.69 0.01 0.51 0.01 0.38

Household size -1.22 -1.12 -1.23 -1.12 -1.01 -0.91 -0.53 -0.36 -0.45 -0.31

Immigrant status 8.96 2.15 ** 9.27 2.22 ** 9.52 2.26 ** 11.03 1.96 ** 10.71 1.90 *

Health status 0.47 0.24 0.74 0.38 0.48 0.24 -2.39 -0.91 -2.85 -1.09

Secondary education -10.60 -1.31 -9.43 -1.16 -8.58 -1.04 -1.35 -0.11 -3.61 -0.31

Tertiary education 26.55 2.98 *** 27.62 3.07 *** 27.99 3.06 *** 35.36 2.61 *** 33.33 2.51 **


Tenure -2.30 -4.59 *** -2.23 -4.43 *** -2.21 -4.34 *** -2.05 -3.06 *** -2.03 -3.05 ***

Tenure squared 0.03 1.70 * 0.02 1.63 0.02 1.56 0.02 1.14 0.02 1.12




Summary• We have identified types of self-employees which are more likely to

be underemployed with regard paid-employees. These groups are the more fragile ones: solo-self-employed, dependent, necessity and freelancers.

• These results seems to be independent (and robust) from the criterion for defining the underemployment (involuntary part time, over-qualification, underpayment, job satisfaction)

• Controls operate in the expected way


Conclusions

• Our results provide new results on the phenomenon of precarious self-employment.

• This phenomenon is closely associated to the emergence of the GIG sector.

• Our results help to characterise to what extent this phenomenon is associated to some types of self-employees and to certain individual characteristics.


Further research

• Check differences after this crisis (Business cycle effects or even structural changes after the Great Recession.

• Industry: checking the existence of differences in the GIG sector, defining activities (NACE correspondence).

• Macro analysis

• Survival analysis


Thank you for your attention !!!


The impact of self-employment on wellbeing

Martin Binder

Bard College Berlin

Levy Economics Institute of Bard College, NY/USA

Introduction/Motivation

• SE in UK: 4.8mio, 15% of the workforce• Most SE are sole traders/solo SE (>75%)

(1) Heterogeneity (“True Diversity” report by CRSE)• Pay, independence, security; opportunity, necessity, freelancer, …

(2) How are the self-employed doing?• “Beyond GDP” moment in SE

• “Big picture view of self-employment”


Dellot 2014

Incompleteness of wellbeing measures

• ... lower average pay (Hamilton, 2000, Sorgner et al., 2017)

• … lower fringe benefits (Storey, 1994)

• … higher earnings variability (van Praag/Versloot, 2007)

• … more stress? (no: Stephan/Roesler, 2010, yes: Schieman et al., 2006)

• … longer working hours (Hyytinen/Ruskanen, 2007)

• … better overall health, but: self-selection (Rietveld, 2017, Baron et al. 2016)

• … higher work satisfaction (Blanchflower, 2004, p. 52)


What we know: SE SWB

• SE are heterogeneous group → no one size fits all assessment• + Andersson, 2008 (Sweden); Craig et al., 2007 (Australia)

• Maybe +: Alesina et al., 2004 (US) and Blanchflower, 2004, depends on subgroups

• Schjoedt/Shaver, 2007, no evidence for US nascent entrepr.

• Binder/Coad, 2013, UK and Binder/Coad, 2016, Germany, only for opportunity SE, Binder, 2017 (Germany): worries determine SWB

• Necessitates tailored policies


• SWB in the UK (BHPS data 1997-2008)


Policies (just a teaser, read the report ☺)

• Number of government initiatives (good!), good business & entrepreneurial culture (compared to e.g. Germany) but most are tailored along income & growth!

• (Re-)Orientation of policies and efforts along…• … Narrative: Not growing as normal case and still worthy of support

• … Non-economic dimensions: health, social, family/work-balance, …


• Example: Stress and worries (mental health)• Related to financial situation: better smoothing of income streams with

improved overdraft/emergency credit lines/crisis mentoring

• Improvements in small claims court/enforcement of contracts

• Improvements in knowledge about pay rates and contractual design

• Stress- and self-management techniques

• Example: Shared co-working spaces (social domain/work-life balance)• … provide community and networking opportunities

• … shared access to accountants, lawyers, child care

• … shared access to training and mentoring

• … social contact


2 May, 2018

Document prepared for

Engaging self-employed people with pensions and life time savings (conducted in collaboration with NEST Insight)

Global Workshop on Freelancing & Self-Employment Research

Jonathan Freeman & Jane Lessiter, i2media research

Thinking about self-employed workers on relatively low incomes (and asset poor), how might access to, and engagement with, a pension provider/pension scheme be increased?

1. What does the journey towards pension provider access and engagement look like for self employed workers, and the subset of 'freelancers'?• Can we identify typical ‘types of experience’ (journeys/touchpoints)?

2. What are the potential touch points at which, and mechanisms by which, this target audience might be engaged to save for their financial futures?

3. What are their thoughts and feelings about a range of potential money management scenarios?

4. What recommendations can be made to inform a research plan for pilot study

Aims and objectives

2 May, 2018 © i2 media research 203

Research Activities


Scope

Phase 1

Consult and explore variables of interest

Phase 2

Explore/Refine

Primary qualitative research

Phase 3

Test

Primary empirical research

We reviewed relevant existing research and conducted one-to-one in-home depth interviews (with informed consent, short questionnaire followed by interview discussion and an incentive of £40)

• A total of 13 interviews (60-90 mins) were conducted between 15 Feb and 9 March 2018 (10/13 recruited via an agency)

• Inclusion criteria and sample frame• 18-54 years• Self employed/freelancer work

• with/without using digital online apps and platforms for work/jobs• Income greater than £10k but less than £26k• Not currently contributing to a pension (may have accrued)• Few tangible assets (i.e., skewed towards non-home owners - asset

poor indicator)• Mix of geographic UK locations

Research Activities


• South East (London, Surrey Borders) n = 6• 4M, 2F, occupations included a delivery driver, holistic therapist,

tree surgeon, carpenter, music industry worker, and freelance writer, most lived with family, 2 lived alone

• North England (Leeds) n = 2• 2F, occupations were Arts and Crafts/clothes designer and

hairdresser, one lived with her mother, and the other with her husband and children

• South West England (Bristol and Devon) n = 5• 3F, 2M, occupations were Health and Safety Officer (under an

umbrella company), 2 chefs (fast food/healthy food catering), 2 Arts and Crafts workers (jewellery, personalised boxes, most lived with family, one lived in shared HH

Interview sample characteristics


There was no single journey in the pensions lifecycle for the S-E

• For most, there were no current actions or concrete plansto engage with making more preparations for their futures

• Low engagement associated with low and high levels of concern

• Journeys of Life: inter-related factors differentially impact transition towards pension engagement...

Q1. The Journey


Broad reasons for being S-E in our sample

“It enables me to live”

• e.g., trapped, low self perception of PAYE employability

• retirement is too much to contemplate, focus is on existence and getting something for nothing, lazy/easy but long work; fair?

• “It enables me to able to have quality of life, right now” • e.g., flexible, perceptions of choice and opportunities

• retirement could be figural, just not now, focus is on today/tomorrow/self/others but also opportunities

“It’s a way of life” • e.g., identity and ‘life force’ derived from S-E activities

• retirement not figural, but might be if the time is right and opportunity arises, focus is on self development

Q1. The Journey


Reasons for being S-E interacted with intention to engage with saving, influenced by...

• Perceived capacity for LT saving• Perceived willingness and priority (incl. familiarity) of LT

saving• Stage of life, and personal relevance/meaning of

life/retirement• Future Gazing (imaginative capacity, fantasy/reality,

stress/anxiety, loss)• Balancing perceptions of certainty/risk and personal

responsibility• Perceived quality of life (economic/non-economic terms)• Accessibility and Usability (meeting unmet needs)

Q1. The Journey


2. What are the potential touch points at which, and mechanisms by which, this target audience might be engaged to save for their financial futures?

• Personal trusted individuals (wisdom)

• Social networks (community)

• Key life events/actions (fantasy vs. reality)

• Routine/simplicity (convenience)

Q2. The Touch Points


3. What are their thoughts and feelings about a range of potential money management scenarios?

Q3. The scenarios


Examples: Scenarios


P1, male, 25-34, Carpentry

P3, male, 35-44, Therapist +

Examples: Scenarios


P8, female, 35-44, CraftP10, female, 35-44, Craft

P4, male, 25-34, Courier

P8, female, 35-44, Craft

• [1] NI increase (to 12%) favoured by 2 (top 3: n=7, bottom 3: n=6)

• [2] Invoice levy favoured by 2 (top 3: n=5; bottom 3: n=7*)

• [3] Side-car bank account favoured by 4* (top 3: n= 8*, bottom 3: n=5*)

• [4] Cashback LT saver favoured by 4* (top 3, n= 8*, bottom 3: n=5)

• [5] Auto-enrolment favoured by 1* (top 3, n=5; bottom 3, n=8*)

• [6] Information support favoured by 2 (top 3, n=6*, bottom 3, n=7*)

Q3. The scenarios


NB. *reflects influence by joint (=) preferences (e.g., where 2 scenarios have equal position)

This research highlights the need to unpick the complex relationships for S-E workers between stress/anxiety, life events, identity and engagement, which has implications for...

• Types of messages, engage the widest target with relevant information

• Timings of messages, engage at right time, not when too late to act

• Types/proportions of people, different preferences for engagement

This research also highlights challenges with effective leverage of user data (GDPR)

Q4. The recommendations


*based on data from reports (NETSPAR and Nest Insight) where available, and inferences on those when missing. It serves the purpose of helping/guiding our choices for the pilot study.

Estimated reliability of engagement


Trackability Efficacy Impact Interaction

Buying a house Medium? Low/medium Economic & Non-economic

Age, relationship status

Getting Married Low/medium Medium Economic & Non-economic

Age, children

Becoming a parent Low/medium Low/medium Economic & Non-economic

Age, other children

Changing jobs Medium/high High Economic & Non-economic

Age

Losing job/redundancy Medium/high Low Economic & Non-economic

Age, dependents, relationship status

Round number birthdays High Low/medium Non-economic Age, relationship status

Divorce Low? Medium Economic & Non-economic

Age, gender, children

Parents/friends retiring Low Medium Economic & Non-economic

Age

Paying off mortgage Medium Medium/high Economic & Non-economic

Age, relationshipstatus

Death of a spouse/family/friend

Low Medium Economic & Non-economic


Inheritance Medium Medium/high Economic & Non-economic


Using a multi-method approach including 13 interviews with low income S-E workers, we identified

• Complexity in the use of life events to predict level of engagement with long term savings and retirement planning, e.g., variation in...• Personal meaning of life events, economics, quality of life and ‘future’• Individual differences in impact of stress and anxiety on engagement

• Qualitative indications of preferences for money management scenarios, to be tested quantitatively and behaviourally

• Need for research to unpick relationships with engagement• Arousal and valence (anxiety/excitement)• Increasing salience of aspects of ‘self’

• Implications for real world RCT (test effectiveness of different solutions)

Summary


Any questions?

Any suggestions?

[email protected]

[email protected]

Thank you


Modern Economy

Free Radicals

A ‘new deal’ for the self-employed

Modern Economy

Context

Free Radicals - methodology

• 4.77m self-employed workers in labour market – 14.8%• Up from 12% in 2001.• Recent tail-off – but resilience suggests structural change• Close to largest share of labour market ever (2016 peak)

• Literature Review – who are the self-employed? • Focus Groups (Leeds and London)• Semi-structured interviews – “vulnerable” groups and policy

experts• Desk-based policy development

Modern Economy

The Big Picture

1. Rising self-employment telling us something profound aboutboth employee experience and changing attitudes to work

2. Outsize obsession with ‘platform economy’ - distorting policydebate.

3. Self-employment portrayed as enforced vulnerability - this iswrong.

4. Heterogeneity - but “squeezed middle” character.5. “Corporatist Bias” across policy debate. First principles

dressed as technocracy

6. Self-employment as de facto flexible working strategy. Is theira pragmatic choice?

Modern Economy

The Self-Employed Experience

Modern Economy

The Self-Employed Experience

“I like not having to get up in the morning, I like not having to not travel. It’s great really – but I would like a bit more money”.

“My experience is that they [banks] do not get it at all.. For example, trying to get a mortgage and things like that. If you don’t have a regular monthly income its ‘what do you get each month?’ Sometimes I get nothing; sometimes I get a big lump sum. I can’t predict and it does not compute for them”

“I worry if I survive like this what happens when I’m older, when there’s nothing in my account – that’s when I panic. I mean, I can sometimes afford to contribute to a private pension, but work is so intermittent.”

“As long as I didn’t mention I was blind to a prospective client, getting work was pretty easy. But If I mentioned it people would not be interested”

Modern Economy

Policies - Savings and Financial Inclusion

Policies - Taylor Report / Platform Economy

• Auto-enrolment scheme. State-backed, 4% top-ups, £800m a year.

• Convening power important - income protectioncollectivisation, fin tech innovation.

• Universal National Insurance (UNI) as model for future?• Savings liquidity as key goal - sidecar model, reform of

Lifetime ISA

• Beefed up enforcement regime - LME needs to be serious position.

• Licence to operate? Worth consideration. • Statutory definition for self-employment. • Portable Benefits and Universal National Insurance (UNI)?

Modern Economy

Policies - Tax

Policies - Support for Vulnerable Workers

• Urgent Reform of Universal Credit• Paternity Pay, Maternity Pay, Shared Parental Leave• Big push on ‘Disability Confident’ procurement - through

supply chain

Policies - Training, working conditions, infrastructure

• Tax relief for training extended to new skills • Enshrine some rights - contract, timely payment - in law• Business rates relief relaxed in low start-up areas

• New ‘Engagers Tax’ - 2.5% rising to 5% and 7.5%. • Corporation Tax reduction halted. • Tax stability - at least until IR35 evidence collection.

Global Workshop on Freelancing & Self-Employment Research

Documents

Transcript of Global Workshop on Freelancing & Self-Employment Research