J. Merz and P. Böhm, Research Institute on Professions (FFB), University of Lueneburg Earnings and...

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J. Merz and P. Böhm, Research Institute on Professions (FFB), University of Lueneburg

Earnings and Working Hour Arrangements

Earnings and Working Hour Arrangements –

A microeconometric analysis for Self-employed and Employees with German Time Use Diary Data

Joachim Merz and Paul Böhm*

Earnings and Working Hour Arrangements –

A microeconometric analysis for Self-employed and Employees with German Time Use Diary Data

Joachim Merz and Paul Böhm*

*Prof. Dr. Joachim Merz, Dipl.-Vw. Paul Böhm, University of Lüneburg, Department of Economics and Social Sciences, Research Institute on Professions (Forschungsinstitut Freie Berufe, FFB), Chair 'Statistics and Professions', Campus Scharnhorststr. 1, 21335 Lüneburg, Germany, Tel: 04131/78-2051, Fax: 04131/78-2059, e-mail: merz@uni-lueneburg.de; http://ffb.uni-lueneburg.de

Central question:

About consequences of working hour arrangements

with regard to daily timing and fragmentation of work time

on income/wages

self-employed: (Liberal) Professions (Freie Berufe)

Entrepreneurs (Tradesmen)

employees

Earnings and Working Hour Arrangements – A microeconometric analysis for Self-employed and Employees with German Time Use Diary Data Joachim Merz and Paul Böhm

1 Some background

2 Data: The German Time Budget Survey 2001/02

3 Daily Working Hour Arrangements – Timing and Fragmentation of Work

4 Timing and Fragmentation of Work and Earnings/Wages: Microeconomic Model Microeconometrics by a Treatment Effects Approach

5 Results

6 Conclusions

Dayli working hours Hamermesh (2002, 1999, 1996)

German and American time use data, GSOEP

Harvey/Fisher/Gershuny/Akbari 2000

Canada, The Netherlands, Norway, Sweden

Callister/Dixon 2001 New Zealand

Merz/Burgert 2004,

Merz/Böhm/Burgert 2005

Timing and fragmentation of work, participation and income, self-employed and employees, German Time Use Study, diaries)

Self-Employment Income Hübler 1991 Professions (Freiberufler), tradesmen (entrepreneurs,

Gewerbetreibende) and employees Parker 1997 Self-employment income in the U.K. Hamilton 2000 Rreturns of self-employment Hirschel 2003, Hirschel/Merz 2004 High income, panelanalysis, Germany Merz 2004 High income, self-employed and employees, German Federal

Poverty and Wealth Report) Merz/Paic 2005 Income determinants, FFB-Onlinesurvey 2004, Germany) Merz/Zwick 2005 High income, professions, entrepreneurs and employees,

Germany)

Working hour arrangements, income and self-employment Erosion of ‚normal’ work day: disintegration of hierachies, loosening of the ‚male breadwinner’ model, growth of part time work, new flexibilities of working time … Alternative working hour arrangements:

firm side argument: flexibility on duration, timing and distribution of working time allows cost efficient reaction due to demand deviations (Gunderson 2002)

employee side: better/harder coordination of social and family life

Self-employment: per se (more) time sovereignity and choice possibilities (Wales 1973)

General Hypotheses Time sovereignity of self-employment result in minor differences with regard to working pattern compared to employees Higher risks of self-employment yields a higher income (Hamilton 2000)

Working hour arrangements and wages

Working hour arrangements result in different income/wage patterns because of

Cost argument: Premium for non-normal working pattern because of extra efforts for atypical working pattern (night and shift work)

Productivity argument: ‚cicardian rhythm’ (Steinhausen, M. 1991) with particular prodictivities at certain times in a day

Hypotheses Less time sovereignity results in higher premia;

more time sovereignity allows better coordination of productive time

Fragmented working days result in higher wages/income

Employees: Atypical working pattern result in higher wages

Self-employment: wage difference between atypical and typical working pattern small

Zeitverwendung in Deutschland 2001/02

The German Time Budget Survey 2001/02

Respondents: Persons ten years and older, German population in private households

Quoted sample, four times the year

No. of households: 5,171

No. of persons with diaries: 11,962

Method: Time diaries in three consecutive days, ten minutes interval

No. of diaries: 35.813

The German Time Budget Survey 2001/02

Main activity with additional information about…

Simultaneous activity

Location of main activity

With/without children

With/without other household members

With/without other person

Personal questionnaire

Household questionnaire

Working hour arrangement dimensions

Timing of daily work

• Mainly within core working time• Mainly outside core working time

→ core working time: 7:00 – 17:00

Fragmentation of daily work

• Not fragmented → one episode, without breaks• Fragmented → two and more episode, with breaks→ Breaks: min. 60 minutes

Working Hour Arrangement Categories 2001/2002 7 am 5 pm

examples n %

0 no work 61.4% 1 mainly core, one episode 25.1% 2 mainly core, more than one episode 9.7% 3 mainly non-core, one episode 2.5% 4 mainly non-core, more than one episode 1.3% Source: German Time Use Study 2001/02

Working Hour Arrangement Categories by Timing of work and Fragmentation in Germany 2001/2002

Timing of work mainly core mainly non-core Total I III one 65.1% 6.5% episode n = 6,884 n = 716

N = 40,503,406 N = 4,037,688

Fragmentation

two or more 25.1% 3.3% episodes n = 2,698 n = 350

N = 15,605,547 N = 2,026,132

n=10,648 Total 90.2% 9.8% N = 62,172,772

Core one episode (category I)

Arbeit FreizeitCore fragmented (category II)

Arbeit Pause Freizeit

Non-core one episode (category III)

Arbeit FreizeitNon-core

fragmented (category IV)

Arbeit Pause Freizeit

Kernel density estimates of monthly net income 0

0 2000 4000 6000 8000netincom

All working Category I

0 2000 4000 6000 8000netincom

All working Category III

0 2000 4000 6000 8000netincom

All working Category II

0 2000 4000 6000 8000netincom

All working Category IV

Net Income: Distributive Measures by Working Hour Arrangement

Working Cat,I

Cat. II Cat. III Cat. IV core core non-core non-core one

episode #episodesss>1)

one episode

#episodes>1 Gini 0.3256 0.3149 0.3348 0.3672 0.2987

Theil Index 0.1817 0.1698 0.1885 0.2322 0.1641

Inequality shares %

59.94 29.82 6.93

90/10 13.5 12.4 12.4 25.0 13.5 n 10,607 6,859 2,689 712 347 N 61,962,57

8 40,360,17

4 15,581,4

94 4,014,101 2,006,809

N in % 100.00 65.14 25.15 6.48 3.24

Net Income: Person Shares by Category within Overall Net Income Deciles (%)

1 2 3 4 5 6 7 8 9 10

Deciles

Category I Category II Category III Category IV

Reading: 21% of Category III people have less than 511 € (First Decile limit)

Participation in Working Hour Arrangements (%)

Timing of work

core Non-core

Cat I Cat III

One Professions 34,4 4,4

Episode Entrepreneurs 38,4 2,5

Employees 69,0 6,9

Fragmentation Cat II Cat IV

Multiple Professions 56,2 5,0

Episodes Entrepreneurs 52,8 6,2

Employees 21,2 2,9 Source: German Time Budget Survey 2001/2002

Mean Monthly Net Income, Weekly Working Hours and Working Hour Arrangements

All Category I Category II

Category III Category IV Categori

es Non-Core/ Core/ Non-Core/ Non-Core/

One episode

fragmented

One episode fragmented Professions Net Income 2.792 2.631 2.991 2.420 1.982 Weekly Hours 49,5 48,4 50,0 46,2 53,3 Entrepreneur

Net Income 1.958 2.047 1.861 1.836 2.277 Weekly Hours 53,7 52,1 54,8 44,1 57,5 Employees Net Income 1.514 1.501 1.616 1.266 1.686 Weekly Hours 37,7 37,3 40,1 33,3 41,1 Source: German Time Budget Survey 2001/2002

Economics: Extended labour supply model Standard labour supply model:

max ( , ), . . ( , )u h y s t y wh v h h w v

h= working hours, y=income, w=wages, v=non-labour income Extensions by main four characteristics:

( | , , ) ' '

( | , , 0)

( | , ) ( | , , 0)*Pr( 0 | , )

Pr( 0 | , )

E h w v structural approach

E h w v h with potential wage

E h w v E h w v h h w v

h w v participation

Endogenous wages (Moffitt 1984)

max ( , ), . . ( )

( , ), ( )

u h y s t y w h h v

h h w v w w h

(Tummers and Woittiez 1991 with a specific utility function)

Pronouncing the ‚extensive margin’ with choice for category j (j=1,…,m)

max ( , ), . . ( )

Pr( ) Pr( , ), ( )

u j y s t y w h h v

j w v w w j

Optimal allocation for arrangement specific choice j (j=1,…,m)

Pr( ) Pr( , , , )

( , , )

j w v z

w w j x

with s and x as further socio-economic variables.

Economics: Human capital earnings function

Basic human capital model:

20ln lnt s p pE E r S ar T br T

tE : capacity earnings in year t

0E : ‚original’ capacity earnings S: years of schooling T: years of job experience

sr : rate of return to schooling

pr : rate of return of job experience

Economics: Human capital wage function

Human capital earnings equation (with observed wages w)

20 1 2ln tw rS T T

Extension of the earnings function (with additional socio-economic vector X)

20 1 2ln t iw rS T T X

Econometrics: Evaluation of social programs

Heckman, LaLonde and Smith 1999

1 0(1 )Y DY D Y D=1 observing oucome 1Y with treatment, D=0 observing 0Y without treatment Effect of treatment of the treated (non-experimental studies)

1 0 1 0

( | , 1) ( | , 1)

( ) ( | , 1)

E X D E Y Y X D

X E U U X D

0 1 0 1 0

[ ( | , 1)]

{ [( ) ( | , 1)]}

Y X D E X D

U D U U E U U X D

U0 and U1 are the before and after treatment error terms

0 1 0 1 0

[ ( ) ( | , 1)]

{ [( ) ( | , 1)]}

Y X D X E U U X D

U D U U E U U X D

non-standard problem: combination of structural parameters with means of the unobservables The coefficient on D is the average outcome gain for participants compared to what they would have experienced in the base state. Simplification by assuming: 1 0U U Everyone with the same X has the same treatment effect (common effect treatment) – yields to Y1-Y0=X(ß1-ß0) and with Y1-Y0=constant (α) to

( ) 0, . . : ,Y X D U E U sel probl corr D U

Treatment effects model

Participation (treatment) from an unobserved latent variable *D as:

1 0, 0 .

D if D D otherwise

Outcome Y X D U

assuming Z independent of error V. D should replaced with something which is not correlated with U => ‘Exclusion restriction’: instrument Z include in the participation equation but excluded from the outcome equation (‘Distance from the training center’, Card 1995).

Treatment effects model with selectivity

Heckman model with common effects The selection bias problem (non-random sampling) occurs if

( | , ) 0E U X D

Rewriting the outcome equation of the treatment model yields

( | , , ) [ ( | , , )]Y X D E U X D Z U E U X D Z

*( | , , )Y X D E U X D Z U

Assuming (U,V) is distributed 2(0, )N yields the selectivity corrected outcome

*( / )UV VY X D U

U* independent and normally distributed and

( ) /[1 ( )]Z Z

as the selectivity correction. Now D may correlate with the outcome equation.

Problems and extensions

- common effect treatment => - heterogeneous impacts (U0 unequal U1) - Indirect effects of programs on non-participants - multiple state model … - semi-parametric versions

Our model:

Heckman type common treatment effects approach

Endogenously chosen binary treatment (selection of working hour arrangement) on endogenous wages

Participation in category j (j=1,…,4)

from an unobserved latent variable *D as:

1 0, 0 .

ij ij j ij

ij ij ij

D if D D otherwise

Outcome Category j specific earnings (wage) function with socio-economic variables and endogenous participation decision:

20 1 2

ln | 1, , , ,

| 1, , , ,

ij ij ij ij ij ij

j j ij j ij j ij i j j j i j ij ij ij ij ij ij

j j ij j ij j ij i j j j i j j j j ij j

E w D S T X Z

r S T jT X D E U D S T X Z

r S T T X D Z

Bivariate Probit equation for category choice with covariance matrix:

cov( , * )1

ij ijj

Difference in expected ln wages between participants and non participants:

ln | 1 ln | 0

(1 )ij

ij ij ij ij j j Ujij ij

E w D E w D

Working hour arrangement Participation probability (Bivariate Probit, j=1,…,4)

Personal Variables Hausehold/Family Variables Age Age2 Family / Social Variables Woman Married Education Number of hh members Elementary Young kids Intermediate Receiving help (in h) Spec. upper or upper Income/wealth situation University Own house Residual hh income Region Partner’s employment East Partner full time work Partner part time work Weekend Constant

Results: Significant different market and non-market impacts

Wage estimates by a treatment effects model – Employees (EM), Entrepreneurs (E) and Professions (P)

Category

I Category

II Category

III Category

IV Core Core Non-core Non-core ln WAGE One episode # episodes One episode # episodes

EM -0,398 ** 0,586 *** -0,470 *** -0,571 ** E -0,752 0,706 -0,780 0,679 Category

j αj P 0,323 -0,040 -0,136 -0,294 EM 0,238 ** -0,336 *** 0,238 *** 0,239 ** E 0,464 -0,505 0,481 -0,225 Hazard

lambda P -0,159 0,057 -0,088 0,026 EM 4654 *** 4264 *** 4969 *** 5135 *** E 96,2 *** 75,5 *** 87,0 *** 101,3 *** Wald

chi2 P 108,8 *** 110,6 *** 112,3 *** 109,0 ***

Category

I Category

II Category

III Category

Core Core Non-core Non-core ln WAGE One episode # episodes One episode # episodes

Human Capital EM 0,106 *** 0,100 *** 0,103 *** 0,104 *** E 0,038 0,042 * 0,030 0,025 School

years (S) P 0,006 0,010 0,011 0,012 EM 0,067 *** 0,069 *** 0,065 *** 0,066 *** E 0,004 0,008 0,019 0,014 Experience

(T) P 0,064 *** 0,062 *** 0,061 *** 0,061 *** EM -0,001 *** -0,001 *** -0,001 *** -0,001 *** E 0,0001 0,00002 -0,0002 -0,0001 Experience²

(T²) P -0,0009 ** -0,0008 ** -0,0008 ** -0,0008 **

Category I

Category II

Category III

Category IV

Core Core Non-core Non-core ln WAGE One episode # episodes One episode # episodes

Personal Variables EM -0,241 *** -0,238 *** -0,271 *** -0,278 *** E -0,193 * -0,145 -0,153 * -0,200 Woman

P -0,127 -0,099 -0,094 -0,096 EM 0,013 0,017 0,009 0,006 E -0,009 -0,023 -0,018 -0,0009 Married

P 0,211 *** 0,221 *** 0,224 *** 0,218 *** Multiple Jobs

EM -0,080 *** -0,073 *** -0,076 *** -0,079 *** E 0,006 0,005 -0,017 0,008 Second

Job P 0,014 0,023 1,158 *** 1,150

Category

I Category

II Category

III Category

Demand Side (Ref: Agriculture) EM 0,126 *** 0,127 *** 0,063 *** 0,076 *** E 0,426 ** 0,356 ** 0,250 ** 0,309 Industry

P -0,251 * -0,196 -0,183 -0,196 EM 0,095 *** 0,080 *** 0,063 *** 0,072 *** E 0,562 *** 0,505 *** 0,485 *** 0,534 Services

P 0,044 0,091 0,104 0,094

Category

I Category

II Category

III Category

Region EM -0,298 *** -0,288 *** -0,332 *** -0,339 *** E -0,209 * -0,245 ** -0,332 *** -0,301 East

Germany P -0,630 *** -0,623 *** -0,620 *** -0,609 *** EM 0,513 *** 0,170 *** 0,395 *** 0,347 *** E 1,401 *** 0,699 1,158 *** 1,150 Constant

P 1,193 ** 1,265 ** 1,236 ** 1,254 **

Overall results Our treatment effects model with the endogenous binary decision to participate in a working hour arrangement category j (treatment) on the continuous wage variable i jY is

highly significant both for self-employed and employees (Wald-Chi²-Test) Daily working hour arrangements and wages results Employees: Timing and fragmentation of work have significant impacts; fragmented core days have positive premia ( j and hazard lambda (λ) are significant)

Self-employment: no significant direct impacts (but further indirect impacts) – higher time sovereignity result in no particular wage impacts

Schooling In general, according to the human capital approach:

Higher education (schooling) will raise income/wage chances: Professions: higher education is required for many professions, thus wage variance should be small.

Entrepreneurs: successful entrpreneurs need particular capabilities (like risk and decision willingness) which might not correlate with the number of school years (Hübler 1991)

Higher education (schooling) will have impacts mainly on employees and to a lesser extent to self-employment

Schooling results Employees: significant (+) Self-employed: not significant

Occupational experience In general, according to the human capital approach:

Higher occupational experience will raise income/wage

Self-employment: reputation and management qualification will raise with growing occupational experience

Positive impacts of a longer/higher experience are expected in particular for self-employment

Occupational experience results Employees: significant (+) Professions: significant (+) Entrepreneurs : not significant

Gender Many personal, firm side and societal factors might result still in lower wages for women; wage discrimination might be lesser for self-employment than for employees (Hübler 1991):

Wage is lower for women than for men Wage differences between men and women are

smaller for the self-employed than for employees Gender results Employees: significant lower wages for women (-) Self-employment : not significant (but entrpreneurs, not fragmented *)

Family To support a family needs higher income; thus individuals might choose better paid jobs than singles:

Wage of married persons is higher than for singles Family status (married) results Employees: not significant Professions: significant (+) Entrepreneurs: not significant

Multiple Jobs Because of a lower specialization workers with multiple jobs are less productive:

Multiple jobs result in lower wages Multiple jobs results Employees: significant (-) Self-employment: not significant

Economic branch, demand side The farming sector is diminishing in favour for the industry and mainly fort he service sector:

Wage in the industry and service sector is higher than in the farming sector

Demand side results Employees: industry and service section significant (+) Professions: not significant Self-employed: industry and service section significant (+) (not cat. IV)

Region Productivity differences between East and West Germany result in different wages:

Wages in East Germany are lower than in West Germany

Region results Employees: significant East Geman lower wages (-) Professions: significant East German Lower Wages (-) Entrepreneurs: only significant in (-) in cat. II and III

Concluding remarks (1)

Economic well-being by adding insights into particular work effort characteristics

- daily timing of work and its fragmentation –

and its resulting wage/income distributive effects

- Employees

- Self-employed as professions and entrepreneurs

- Treatment effects approach

Descriptive results (all workers)On average: Working hour arrangements with fragmented work days, categories II and IV): they work longer, have a higher wage rate and thus an above–average income

Distribution: All non-normal working hour arrangements (categories II,II,IV) compared to he normal situation (category I) show higher inequalities with regard to hours worked, wage paid, and income; one exception: the most irregular working hour arrangement (category IV) shows the most equally distributed income.The most unequal net income distribution: category III (non-core/one episode) with the most unequal working hours distribution. The descriptive distributive analysis thus has shown that timing and fragmentation of work time do have distinct consequences on the earnings distribution.

Concluding remarks (3)Microeconometric results Estimates with endogenous self-selection (treatment effects approach) explaining

wages and participation (bivariate probit-approach) in different daily working hour arrangements support our interdependent two stage modelling strategy with the overall result:

• Employee wages in Germany are dependent on and significant different with regard to the daily working hour arrangement capturing timing and fragmentation of work.

• Self-employment: higher time sovereignity result in no particular wage impacts

• The participation probability for the core/non-core and number of episodes working time categories follow different explanatory pattern with regard to

personal characteristics (demographics, human capital, education, occupational status, multiple jobs, non-market time use), demand side (business sectors), partner’s employment,household characteristics (composition, wealth) as well as a regional indicator.

Further Wage results

Human capital, schoolingEmployees: Higher education raises wages significantly (+)Professions: Higher education is required for many professions (no

effect)Self-employment: Particular capabilities (like risk and decision willingness)

might not correlate with schooling (not sgnificant)

Human capital, occupational experienceEmployees: Higher occupational experience will raise income/wage

(significant (+))Professions: Reputation and management qualification will raise with

growing occupational experience (significant (+))Entrepreneurs : … (not significant)

Gender

Employees: Significant lower wages for women (-)

Self-employment: Not significant (but entrpreneurs, not fragmented *)

Family status (married)

Employees: No family status influence (not significant)

Professions: Family background important (significant (+))

Entrepreneurs: … not significant

Multiple jobs results

Employees: Lower specialisation with multiple jobs is less productive: lower wage (significant (-))

Self-employment: Not specific, not significant

Economic branch, demand sideEmployees: Iindustry and service section significant (+)Professions: Not significantSelf-employed: Industry and service section significant (+) (not cat. IV)

RegionEmployees: Different productivities result in lower wages: significant East

Geman lower wages (-)Professions:…significant East German Lower Wages (-)Entrepreneurs: …only significant in (-) in cat. II and III

• The detailed findings support targeted modern economic and social policy with regard to non-traditional labour market situation and flexibility.

• Further research should deepen these findings and compare them with German time use data of the beginning 90s to disentangle dynamics of flexible labour market situations.

Overall: Significant differences in working hour arrangements and dependent wages between the Self-employed - with Professions and Entrepreneurs - and Employees

… Many thanks !

Contact: Prof. Dr. Joachim Merz merz@uni-lueneburg.de Dipl.-Vw. Paul Böhm boehm@uni-lueneburg.de

http://ffb.uni-lueneburg.de

J. Merz and P. Böhm, Research Institute on Professions (FFB), University of Lueneburg Earnings and...

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