Centre for Market and Public Organisation

1
Centre for Market and Public Organisation
How important is pro-social behaviour in the
delivery of public services? Paul Gregg, Paul
Grout, Anita Ratcliffe Sarah Smith and Frank
Windmeijer University of Bristol, CMPO
2
Overview

• Growing literature emphasising importance of
  intrinsic motivation for workers in the
  non-profit sector
• Is there evidence that workers in the non-profit
  sector (public and not-for-profit sectors)
  provide more effort in the form of donated labour
  than workers in private sector?
• Does the sector affect behaviour?
• Analysis of British Household Panel Survey on
  prevalence of doing unpaid overtime

3
Policy background

• Importance of contracting out of public services
  to private and not-for-profit providers
• Possibility of pro-social behaviour unique to
  non-profit provision is an important dimension in
  the debate about who should provide public
  services

Total spending on services Procurement of services .. Of which Frontline service delivery Of which Facilities (catering, cleaning)
Health 88.7 bn 20.7 bn 43 41
Education 61.1 bn 5.4 bn 46 24
Local govt services 24.9 bn 4.9 bn 83 4
Source Oxford Economics, 2008
4

• Growing literature emphasizing that workers in
  the non-profit sector (public sector and
  not-for-profit sector) may be intrinsically
  motivated to work
• Common emphasis on association between sector of
  employment and pro-social behaviour differences
  in mechanism through which this arises
• Institutional form (Francois, 2000)
• Intrinsically motivated individuals will choose
  not to donate their labour in a for-profit firm
  because the firm will respond by reducing other
  inputs in order to increase profit
• In a not-for-profit firm, the non-distribution
  constraint means that extra effort benefits
  service users. In the public sector,
  bureaucratic budget-setting has the same effect
• Institution matters individuals donate labour in
  a non-profit firm, not in a for-profit firm
• Mission-matching (Besley and Ghatak, 2005)
• Individuals differ in the degree to which they
  care (mission)
• They will donate labour in whatever sector they
  work in, but will be attracted to perceived
  caring sectors (non-profit)

5

• Empirical research
• Evidence that public service motivation is
  reflected in what people say
• Interviews reported in the Guardian Does
  motivation vary by sector?
• Children's service manager, NFP I couldn't do
  it for profit
• Children's strategy manager, Public I wouldn't
  move into the private sector, because at the end
  of the day it's a business
• Housing worker, Public I prefer to be with the
  state sector because you can do more to help
  people. With the private sector .. there's more
  of a profit angle
• British Social Attitudes Survey (John and
  Johnson, 2008)
• Public sector employees are twice as likely as
  private sector employees to say that it is very
  important to them that a job is useful to society
  (32 compared to 15)
• More likely to say that it is very important that
  a job allows them to help other people (27
  compared with 19)

6

• Possible halo effect?
• Important to look at what people do, not just
  what they say
• Rotolo and Wilson (2005) civic-mindedness of
  public sector workers highlighted by greater
  propensity to volunteer
• Frank and Lewis (2004) higher level of
  (self-reported) effort in public sector, but
  public sector defined by industry
• Mokan and Tekin (2005) detailed study of
  childcare industry using rich employer-employee
  matched data set. Whether people value an
  important job has a negative and significant
  effect on wages only in the non-profit sector.

7
Our contribution

• Do people working in the non-profit sector donate
  more labour than people working in the for-profit
  sector?
• Estimate probability of doing unpaid overtime by
  sector of employment including numerous controls
  for individual and job characteristics
• If so, what is the likely mechanism institution
  or selection?
• Estimate fixed effects model to see what happens
  to the probability of doing unpaid overtime when
  people change sector
• Look at behaviour of switchers

8
Data

• British Household Panel Survey
• Followed gt 10,000 individuals each year since
  1991
• Our sample covers 1993-2000 (matched with wage
  information at the occupation level from the
  Labour Force Survey)
• Select full-time employees (30 hours). No
  differential selection by sector.
• 24135 obs aged 16-60 6,601 individuals

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Some definitions

• Measure of donated labour unpaid overtime
• Thinking about your (main) job, how many hours
  excluding overtime and meal breaks are you
  expected to work in a normal week?
• And how many hours overtime do you usually work
  in a normal week?
• How much of that overtime (usually worked) is
  usually paid overtime?
• 27 individuals do unpaid overtime in BHPS 29
  in LFS

10
Some definitions

• Sector of employment
• Which of the types of organisations do you work
  for (in your main job)?
• For-profit private firm/ company
• Non-profit civil servant/central government,
  local government/town hall, NHS or higher
  education, nationalised industry, non-profit
  organisation
• No significant differences in behaviour between
  individuals working in the public and
  not-for-profit sectors, although nfp sector is
  small

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Some definitions

• Caring services based on industry classification
  (SIC1980)
• Caring health, education, social care (17
  sample)
• Non-caring all other industries
• Results robust to alternative definitions
• Narrower cross-classifying industry with
  occupation to include only managers, natural
  scientists, health teaching professionals and
  childcare workers (14 sample)
• Wider including RD, the arts culture
  corresponding to industries where NfPs are
  located according to Rose-Ackerman, 1996 (20
  sample)

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Distribution by sector
Full sample Health Education Social care
Non-profit caring 14.80 80.04 88.60 83.91
For-profit caring 2.70 19.96 11.40 16.09
Non-profit non-caring 13.34
For-profit non-caring 69.16
Total 24135 1473 1825 926
Non-profit refers to not-for-profit organisations and public organisations For-profit refers to private firms Caring refers to health, education and social care Non-caring refers to all other industries Non-profit refers to not-for-profit organisations and public organisations For-profit refers to private firms Caring refers to health, education and social care Non-caring refers to all other industries Non-profit refers to not-for-profit organisations and public organisations For-profit refers to private firms Caring refers to health, education and social care Non-caring refers to all other industries Non-profit refers to not-for-profit organisations and public organisations For-profit refers to private firms Caring refers to health, education and social care Non-caring refers to all other industries Non-profit refers to not-for-profit organisations and public organisations For-profit refers to private firms Caring refers to health, education and social care Non-caring refers to all other industries Non-profit refers to not-for-profit organisations and public organisations For-profit refers to private firms Caring refers to health, education and social care Non-caring refers to all other industries
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Unpaid overtime, by sector of employment  
Proportion of sample Proportion doing unpaid overtime Mean hours unpaid OT (gt0)
Caring services, non-profit 0.148 0.46 9.59
Caring services, for-profit 0.027 0.29 8.34
Other industries, non-profit 0.133 0.22 6.56
Other industries, for-profit 0.692 0.24 8.49
Full-time employees caring services health, education and social care Indicates that the difference to caring services, non-profit is significantly different at 5 level Full-time employees caring services health, education and social care Indicates that the difference to caring services, non-profit is significantly different at 5 level Full-time employees caring services health, education and social care Indicates that the difference to caring services, non-profit is significantly different at 5 level Full-time employees caring services health, education and social care Indicates that the difference to caring services, non-profit is significantly different at 5 level
14
Proportion doing unpaid overtime, by sector of
employment  
Public sector Not-for-profit
All Caring services 0.46 0.49
Non-profit For-profit
Health 0.31 0.23
Education 0.59 0.47
Social care 0.43 0.15
Women 0.46 0.27
Men 0.46 0.34
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Pooled regression model

• Dit dummy variable if individual i, i1,,N,
  does any unpaid overtime in time t
• Sectorit set of four binary indicators
  representing the non-profit and for-profit
  caring sectors and the non-profit and
  for-profit non-caring sectors
• x vector of individual characteristics age,
  age-squared, gender, married, presence of
  children, age of youngest child (interacted with
  gender), education, ethnicity
• z vector of job characteristics wage measures,
  contracted hours, trade union present (and
  whether the individual is a member), pension
  scheme (and whether the individual is a member),
  individual is a manager, workplace size,
  indicators for industry (health, education and
  social care)
• Region and year dummies

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Career concerns

• Individuals motivated to do unpaid overtime by
  the prospect of higher future remuneration, i.e.
  career concerns
• Bell and Freeman (2001) show that hours worked
  are positively related to occupational wage
  dispersion (measured by standard deviation of ln
  hourly earnings)
• We take a similar approach but look at
  age-relevant part of wage distribution, i.e.
  16-60 for individuals aged 16-30, 30-60 for
  individuals aged 30-45 and 45-60 for individuals
  aged 45-60
• Both within occupation, across sectors and within
  occupation, within sectors

17
Career concerns

• Since current wage may reflect past unpaid
  overtime (and be correlated with current unpaid
  overtime), instrument using ln of median wages by
  occupation/ year and age group
• Opportunity cost, income effect and/or career
  concerns
• Other variables to capture career concerns
• Quadratic in years tenure in current job
• Indicator Do you have an opportunity for
  promotion in your current job?
• Indicator Does your pay include a bonus?
• Indicator Whether individuals are satisfied
  with job security

18
Results for pooled linear probability
model Dependent variable whether individual
does unpaid overtime (0/1)
(1) (2) (3)
For-profit, caring (omitted)
Non-profit, caring 0.174 (0.032) 0.139 (0.032) 0.123 (0.027)
Non-profit, non-caring -0.062 (0.031) -0.053 (0.030) -0.148 (0.032)
For-profit, non-caring -0.045 (0.029) 0.003 (0.027) -0.118 (0.030)
ln wage occ/age/year 0.326 (0.015)
SD ln wage, occ/age/year 0.364 (0.048)
Control variables No controls Controls for age, educ, gender, marital status, children, ethnicity Additional controls for job tenure, promotion opportunities, bonus pay, job satisfaction, manager, firm size, unionisation, usual hours, industry
N (indivs) 24135 (6016) 24135 (6016) 24135 (6016)
Standard errors are clustered at the individual level. significant at the 1 level Standard errors are clustered at the individual level. significant at the 1 level Standard errors are clustered at the individual level. significant at the 1 level Standard errors are clustered at the individual level. significant at the 1 level
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Hours worked, by sector of employment  
Proportion of sample Contracted hours unpaid OT Proportion doing paid overtime Mean hours paid OT (gt0) Contracted hours unpaid OT paid OT
Caring services, non-profit 0.148 41.44 0.10 7.90 42.22
Caring services, for-profit 0.027 40.34 0.22 7.21 42.10
Other industries, non-profit 0.133 39.53 0.26 8.21 41.66
Other industries, for-profit 0.692 41.32 0.34 8.51 44.20
Full-time employees caring services health, education and social care Indicates that the difference to caring services, non-profit is significantly different at 5 level Full-time employees caring services health, education and social care Indicates that the difference to caring services, non-profit is significantly different at 5 level Full-time employees caring services health, education and social care Indicates that the difference to caring services, non-profit is significantly different at 5 level Full-time employees caring services health, education and social care Indicates that the difference to caring services, non-profit is significantly different at 5 level Full-time employees caring services health, education and social care Indicates that the difference to caring services, non-profit is significantly different at 5 level Full-time employees caring services health, education and social care Indicates that the difference to caring services, non-profit is significantly different at 5 level
20
Results for pooled OLS model Dependent variable
ln total hours
Basic hours unpaid OT Basic hours unpaid OT paid OT
For-profit, caring (omitted)
Non-profit caring 0.0234 (0.011) 0.0003 (0.011)
Non-profit, non-caring -0.0633 (0.012) -0.0632 (0.013)
For-profit, non-caring -0.0451 (0.012) -0.0235 (0.012)
Control variables Full set of individual and job controls Full set of individual and job controls
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• Individuals in the non-profit caring sector are
  significantly more likely to do unpaid overtime
  than in the for-profit caring sector
• Not a general non-profit effect applies only in
  caring industries
• But they do not work more hours in total they do
  less paid overtime
• Is unpaid overtime voluntary donated labour or
  just a social norm?
• Use fixed effects regression to look at what
  happens when people change sector

22
Fixed effects regression model
Table 5. Switches across sectors
Sector, time t Sector, time t Sector, time t Sector, time t
Sector, time t 1 Non-profit caring For-profit caring Non-profit noncaring For-profit Noncaring
N-P caring 2404 83 135 50
F-P caring 80 288 5 88
N-P noncaring 129 9 2224 184
F-P noncaring 88 85 133 12099
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Results for fixed effects linear probability
model Dependent variable whether individual
does unpaid overtime (0/1)
(1) (2) (3)
For-profit, caring (omitted)
Non-profit, caring 0.000 (0.029) -0.001 (0.028) 0.002 (0.028)
Non-profit, non-caring -0.042 (0.030) -0.039 (0.030) -0.061 (0.042)
For-profit, non-caring -0.015 (0.027) -0.015 (0.027) -0.037 (0.041)
Ln wage occ/age/year 0.092 (0.017)
SD ln wage, occ/age/year 0.110 (0.040)
Control variables No controls Controls for age, educ, gender, marital status, children, ethnicity Additional controls for job tenure, promotion opportunities, bonus pay, job satisfaction, manager, firm size, unionisation, usual hours, industry
N (indivs) 22703 (4619) 22703 (4619) 22703 (4619)
Standard errors are clustered at the individual level. significant at the 1 level Standard errors are clustered at the individual level. significant at the 1 level Standard errors are clustered at the individual level. significant at the 1 level Standard errors are clustered at the individual level. significant at the 1 level
24
Results for fixed effects linear probability
model Dependent variable whether individual
does unpaid overtime (0/1)
(1)
For-profit, caring (omitted)
First period For-profit caring -.0247 (.0339)
First period Non-profit caring -.0138 (.0378)
Subsequent periods Non-profit caring .0019 (.0361)
First period Noncaring -.0320 (.0461)
Subsequent periods Noncaring -.0683 (.0477)
Control variables Additional controls for job tenure, promotion opportunities, bonus pay, job satisfaction, manager, firm size, unionisation, usual hours, industry
N (indivs) 22703 (4619)
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Fixed effects results

• Insufficient switchers?
• Estimated coefficient is (close to) zero, rather
  than being imprecisely estimated
• Measurement error?
• Would have to be very high (around a half) to
  generate our observed results
• 75 of switchers (from n-p care to f-p care or
  v.v) stay in next sector for at least two periods
  (i.e. not just one-off mis-reporting)
• Other sector coefficients are non-zero

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Fixed effects results

• No change in behaviour on switching
• Evidence against social norms since individuals
  would change to comply with behaviour in new
  sector
• But also inconsistent with strong organisational
  form explanation (Francois, 2000) individuals
  donate labour in non-profit, but not in
  for-profit sector
• Is there any evidence to support a selection
  story?

27
Evidence on selection

• Compare the switchers with the stayers
• For people working in the non-profit caring
  sector
• Do people who (ever) switch from the non-profit
  sector to the for-profit sector or the non-caring
  sector donate less labour when they are in the
  non-profit sector than people who stay?
• For people working in the for-profit caring
  sector
• Do people who (ever) switch from the for-profit
  sector to the non-profit sector donate more
  labour when they are in the for-profit sector
  than people who stay?

28
Evidence on selection
Estimation results for linear probability
model Dependent variable whether individual
does unpaid overtime (0/1)
Employees in the non-profit caring sector Employees in the non-profit caring sector Employees in the for-profit caring sector Employees in the for-profit caring sector
Switch to for-profit caring -0.132 -0.114
(0.075) (0.058)
Switch to non-profit caring 0.078 0.039
(0.089) (0.069)
Switch to non-caring -0.141 -0.064 -0.053 0.025
(0.052) (0.044) (0.076) (0.068)
Control variables No Yes No Yes
N 3134 3134 517 517
Robust standard errors are clustered at the individual level indicates significant at 1 level, at 5 level, at 10 level Robust standard errors are clustered at the individual level indicates significant at 1 level, at 5 level, at 10 level Robust standard errors are clustered at the individual level indicates significant at 1 level, at 5 level, at 10 level Robust standard errors are clustered at the individual level indicates significant at 1 level, at 5 level, at 10 level Robust standard errors are clustered at the individual level indicates significant at 1 level, at 5 level, at 10 level
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Conclusions

• Evidence of an association between institutions
  and donated labour
• Institutions appear to work through selection
  rather than incentives
• Possible that the behaviour of some people may be
  affected by the sector that they work in, but we
  dont observe them switching
• An exogenous change in institution might be more
  convincing to identify the effect of sector,
  although selection would still be important
• Sample sizes limit the extent to which we can
  carry out more detailed analysis of switchers
• What is it about the non-profit sector that
  attracts pro-socially motivated people?