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

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

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

• 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)

• 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%)

• 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.

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”

Data

• British Household Panel Survey

• Followed > 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

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

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

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 R&D, the arts & culture corresponding to industries where NfPs are located according to Rose-Ackerman, 1996 (=20% sample)

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

Distribution by sector

Unpaid overtime, by sector of employment 

Proportion of sample

Proportion doing unpaid

overtime

Mean hours unpaid OT

(>0)

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

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*

Pooled regression model

• Dit = dummy variable if individual i, i=1,…,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

4

1it s it it it it

s

D sector s x z u

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

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 jobIndicator: 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

Results for pooled linear probability modelDependent 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

Hours worked, by sector of employment 

Proportion of sample

Contracted hours +

unpaid OT

Proportion doing paid overtime

Mean hours paid OT

(>0)

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

Results for pooled OLS modelDependent 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

• 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

Fixed effects regression model

Table 5. Switches across sectors

Sector, time t

Sector,time t – 1

Non-profit caring

For-profit caring

Non-profit noncaring

For-profitNoncaring

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

4

1it s it it it i it

s

D sector s x z v

Results for fixed effects linear probability modelDependent 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

Results for fixed effects linear probability modelDependent 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)

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

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?

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?

11 12 ' 'NPC FPC NCit i i it it itD Switch Switch x z u

itititNCi

NPCi

FPCit uzxSwitchSwitchD ''2221

Evidence on selection

Estimation results for linear probability modelDependent variable : whether individual does unpaid overtime (0/1)

Employees in the non-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 517

Robust standard errors are clustered at the individual level*** indicates significant at 1% level, ** at 5% level, * at 10% level

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 don’t 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?