04.24.2013 - Maitreesh Ghatak

Motivating Knowledge Agents:
Incentive Pay vs Social Distance
Maitreesh Ghatak (LSE)
Erlend Berg (Oxford)
R Manjula (ISEC)
D Rajasekhar (ISEC)
Sanchari Roy (Warwick)
IFPRI, 24 April 2013

Introduction Theory Empirics Discussion and conclusion
Motivation
Public services in developing countries are often dysfunctional
Schools, health care, contract enforcement, social protection. . .
A body of work on supply-side constraints
But little is known about demand-side constraints
We hypothesise that intended beneﬁciaries often don’t have enough
information to be able to beneﬁt from a service
Information costs responsible for low take-up of welfare schemes in
developed countries (Aizer 2007; Daponte et al 1999)
In India, awareness about the National Rural Employment Guarantee
(NREG) is very low in some of the poorer states
Maitreesh Ghatak (LSE) Motivating knowledge agents

We look at two aspects of information transmission
Incentives
Performance pay is rare in the public sector (for several reasons)
Hence, little is known about the role of incentives in spreading
awareness of government schemes
Social barriers
Evidence that public goods are under-provided in fragmented societies
(Easterly & Levine 1997; Kimenyi 2006)
Possibly because people prefer to interact with ‘their own kind’
(Banerjee & Munshi 2004)
If so, information may not easily cross social boundaries
May explain heterogeneity in programme awareness
Interaction eﬀects: Do incentives alleviate, or exacerbate, the
potentially negative eﬀects of social barriers?

What we do
Develop a simple model of motivated agents (Besley and Ghatak,
2005) to generate predictions on impact of incentive pay and social
distance
Run a randomised experiment in which we hire agents to spread
information about a government welfare programme
Aim to answer the following questions:
1 Do ‘knowledge agents’ actually improve programme knowledge?
2 Does incentive pay make a difference?
3 Does improved programme knowledge translate into higher programme
take-up?
4 Does social distance between agent and target household have an
effect on knowledge transmission?
5 Does incentive pay reinforce or weaken any social distance effect?

What we find
Hiring agents to spread information has a positive impact on the level
of knowledge of the scheme in the village population
The effect is driven entirely by agents on incentive-pay contracts
In turn, improved knowledge increases programme take-up
. . . establishing that information costs are an impediment to demand
Social distance between agent and beneficiary has a negative impact
on knowledge transmission
Incentive pay seems to cancel out negative effect of social distance
. . . but incentive pay has no impact on knowledge transmission for
socially proximate agent-beneficiary pairs
No evidence of ‘crowding out’

Theoretical framework
Aim: A model to look at interactions of intrinsic preferences and
incentive pay in determining effort
An agent exerts effort in a task (later, two tasks)
Later we will think of each household, or group of households, as a task
Success in a task is binary. Probability of success depends on agent’s
effort
Think of success as having the household’s knowledge about the
government scheme exceed a certain threshold
Alternatively, the task is successful if the household signs up for the
scheme
The principal values success in the task
Observes task outcome, but not effort

Let e be the unobservable eﬀort exerted by agent
The outcome variable Y is binary and 0 and 1 denote ‘failure’ and
‘success’ respectively
The probability of success is p(e) = e

The probability of success is bounded: 0 < e ≤ e ≤ e < 1
Both principal and agent are risk neutral but agents are poor, so
limited liability (no fines)
The agent’s disutility of effort is 1
2ce2 for constant c
If project succeeds, the agent receives a non-pecuniary pay-off of θ
(this is her intrinsic motivation) and the principal receives a pay-off of
π (which may have a pecuniary as well as a non-pecuniary
component)
We assume that the principal’s pay-off incorporates the direct pay-off
of the beneficiaries as well as how the rest of society values their
welfare

Let w be the pay that the principal oﬀers to the agent in the case of
success, and w, the pay in the case of failure
Then b ≡ w − w can be interpreted as bonus pay with w as the ﬁxed
wage component
The agent’s objective is to maximise:
max
e
(θ + w)e + w(1 − e) −
1
2
ce2
This yields the solution:
e = max min{
b + θ
c
, e}, e

The one-task solution
e
e
b
e

Two tasks
Extend basic model: Two tasks
Interpretation: Trying to increase the knowledge among two different
types of households
Unlike the classic multi-tasking model, the outcomes associated with
the tasks are here assumed to be equally measurable
Instead, the differences between the two tasks are in terms of:
1 the agent’s intrinsic pay-off for success for each task, θ1 and θ2
2 the cost-of-effort parameters, c1 and c2
Assume without loss of generality that task 1 is the ‘easier’ task,
c1 < c2

The bonus cannot vary across tasks
We assume that the principal is constrained to oﬀer the same
conditional payments, w and w, for the two tasks
May be politically, socially, or legally constrained to oﬀer the same pay
for all tasks
Relevant characteristics of the agent and/or tasks may not be
observable to principal (e.g. favourite students)

A solution without crowding out (relatively low
substitutability)
e
e
b
e1
e2

A solution with temporary satiation in e2 (intermediate
substitutability)
e
e
b
e1
e2

A solution with crowding out (relatively high
substitutability)
e
e
b
e1
e2

The intrinsically preferred task
Define intrinsically preferred task
The task that receives the most effort from the agent in the absence of
incentive pay
The intrinsically preferred task is not necessarily the one for which
intrinsic pay-off is the greatest
Intrinsic pay-off could be outweighed by greater cost of effort
Conceptually classify household into two categories:
Households similar to agent in terms of social characteristics (‘own’
group)
Households socially distant from agent (‘other’ or ‘cross’-group)
Map the agent’s ‘own’ group to the intrinsically preferred task

The picture emerging from the theory
In the absence of incentive pay, the agent’s own group is preferred
When bonus pay is introduced:
Total effort (weakly) increases
Effort in the easier task increases at least as much as effort in the
harder task
If the two tasks are relatively complementary: Effort in both tasks
increases
If the two tasks are relatively substitutable: Effort in the easier task
increases, effort in the harder task decreases → crowding out
Effort can saturate at lower or upper bounds
If so, may not respond to incentives

Experimental context
The experiment was conducted in the context of an Indian
public-private health insurance scheme for the poor called ‘Rashtriya
Swasthya Bima Yojana’—henceforth, RSBY
Set in two districts in the south Indian state of Karnataka: Shimoga
and Bangalore Rural
The scheme was launched in these districts in Feb–March 2010
Key features of programme:
Eligibility criterion: Below-Poverty-Line (BPL) designation
Covers hospitalization expenses for 700 speciﬁed medical conditions
and procedures
Annual expenditure cap of 30,000 rupees (600 USD) per household
Policy underwritten by insurance company selected in state-wide tender

Experimental design
151 randomly selected villages in Bangalore Rural and Shimoga
Three experimental groups
Flat-pay group: local agent recruited and paid ﬁxed amount 400
rupees every three months (38 villages)
Incentive-pay group: local agent recruited and paid a ﬁxed amount of
200 rupees, plus a bonus depending on the level of RSBY knowledge
amongst the eligible households in her village (74 villages)
Control group: no agent appointed (39 villages)
All agents are female and live locally. Many are members of a local
Self-Help Group (SHG)
Agent’s task: spread information about RSBY among eligible
households

Experimental design
Average pay designed to equal 400 rupees across the two treatment
groups
That is, the average bonus was 200 rupees
The aim was to isolate the ‘incentive effect’ of the contract structure
from the ‘income effect’
Payment structure revealed to agent after recruitment
The aim was to isolate the ‘incentive’ effect of contract structure from
potential ‘selection’ effect
Attrition could re-introduce selection bias, but no agent quit after
being told about the payment structure
Four agents quit a few months later, one due to pregnancy and three
due to migration
Those villages excluded from our analysis
Final number of villages in our sample is 147

Data
Three waves of surveys conducted post-intervention
A random sample of eligible household in our sample villages were
interviewed in each wave, leading to a partial overlap
A few months’ gap between each wave
Aim of the surveys:
Administer knowledge test to eligible households to determine level of
knowledge about RSBY (also used to pay agent)
Measure take-up of RSBY
Collect limited background information on households
Each knowledge test consisted of 8 questions relating to RSBY (score
0–8)
Main outcome variable is the knowledge-test z-scores; we also look at
enrolment in the scheme

Agent summary statistics
Flat pay Incentive pay Diﬀerence
Agent age 34.8 34.8 0.018
(8.81) (8.08) (1.69)
Agent is married 0.81 0.92 0.10
(0.40) (0.28) (0.066)
Agent is of forward/dominant caste 0.43 0.35 -0.080
(0.50) (0.48) (0.099)
Agent’s household head has completed 0.62 0.56 -0.058
primary school (0.49) (0.50) (0.10)
Agent household has ration card 0.89 0.79 -0.10
(0.31) (0.41) (0.077)
Agent owns her home 0.86 0.87 0.0084
(0.35) (0.34) (0.069)
Agent is Self-Help Group president 0.30 0.28 -0.016
(0.46) (0.45) (0.093)
Agent autonomy score (the higher, 5.57 5.68 0.11
the more autonomous) (0.93) (0.84) (0.18)
Agent pay in round 1 400 507.7 107.7
(0) (478.5) (78.8)
Agent pay in round 2 400 403.0 3.04
(0) (209.1) (34.5)
Observations 37 71
[para,ﬂushleft] Note: Standard
deviations/errors in parentheses.Maitreesh Ghatak (LSE) Motivating knowledge agents

Household summary statistics
Control Flat Inc’tive Flat Inc’tive Inc’tive
pay pay −Control −Control −Flat
Household is of forward/ 0.25 0.18 0.17 -0.070 -0.084∗
-0.015
dominant caste (0.43) (0.39) (0.37) (0.054) (0.046) (0.041)
Household head has com- 0.30 0.25 0.31 -0.051 0.011 0.062∗
pleted primary school (0.46) (0.43) (0.46) (0.042) (0.036) (0.035)
Household has ration card 0.94 0.94 0.92 0.00078 -0.019 -0.020
(0.25) (0.24) (0.28) (0.020) (0.023) (0.022)
Household owns its home 0.67 0.64 0.68 -0.023 0.017 0.040
(0.47) (0.48) (0.47) (0.047) (0.035) (0.045)
Observations 375 348 625
Notes: Standard errors are in parentheses and p-values in brackets

Empirical specification
Basic specification: Yhv = α + βTreatv + ehv
Outcome is test z-score; β captures overall effect of knowledge agents
All regressions are weighted least squares
Not all households observed in every wave, but there is overlap
Weighted least squares with total weight 1 assigned to each household
Standard errors are robust and clustered at village level
Survey (wave) and taluk fixed effects included
Taluks are sub-district administrative divisions
4 in Bangalore Rural, 7 in Shimoga

Impact of agents on knowledge
(1) (2) (3)
Knowledge Knowledge Knowledge
Agent in village 0.175*** 0.187***
(0.0645) (0.0572)
Flat-pay agent in village 0.0722
(0.0919)
Incentive-pay agent in village 0.246***
(0.0569)
Survey wave fixed effects No Yes Yes
Taluk fixed effects No Yes Yes
t-test: flat=incentivised (p-value) 0.0600
Notes: Standard errors, in parentheses, are clustered at the village level. * p<0.10, ** p<0.05, *** p<0.01

Pricing out prejudice? Incentives vs social distance
Recent work has shown social distance (identity) to be an important
deteminant of insurance take-up
Do incentives reinforce or weaken the role of social distance?
Construct a metric of social distance based on:
Forward/dominant caste status (0/1)
Whether household head has completed primary school (0/1),
Ration card status (0/1)
Home ownership (0/1)
Deﬁne social distance between agent and household as the absolute
diﬀerence in the agent’s and the household’s characteristics
Construct ‘composite’ social distance as the sum of the four
individual distance measures

Specification: Yhv = α + βDhv + γTv + δDhv ∗ Tv + πX + uhv
Within-treatment analysis, with flat-pay villages as comparison
(control villages necessarily dropped)
β captures the effect of social distance on knowledge when the agent
is not incentivised
γ captures the effect of incentive pay for socially proximate
(non-distant) agent-household pairs
δ captures the differential effect of incentive pay for socially distant
agent-household pairs relative to socially proximate ones

(1) (2) (3) (4) (5) (6)
Knowledge
Incentive pay 0.16* -0.13 0.01 0.19 0.09 0.08
(0.09) (0.14) (0.11) (0.12) (0.09) (0.11)
Social distance -0.66*** -0.38*** 0.09 -0.27** -0.21*
(0.21) (0.10) (0.09) (0.13) (0.12)
Incentive pay x 0.79*** 0.37*** -0.05 0.38** 0.27**
social distance (0.22) (0.13) (0.11) (0.14) (0.12)
Agent, village and Yes Yes Yes Yes Yes Yes
household characteristics
Time and region Yes Yes Yes Yes Yes Yes
ﬁxed eﬀects
Observations 2900 2900 2900 2900 2900 2900
Social distance metric N/A Compo- Caste Educ- Ration Home
site only ation card ownership
only only only
Notes: Standard errors, in parentheses, are clustered at the village level. * p<0.10, ** p<0.05, *** p<0.01

Relating empirics to theory
es(b) denotes effort of agent when dealing with her own social group
eo(b) denote effort when dealing with the other group
Empirically observe four points: es(0), es(b ), eo(0) and eo(b )
The key empirical findings can be summed up as follows:
eo(0) < eo(b ) = es(b ) = es(0)

Relating empirics to theory
Suggests that for own group, agents were already choosing maximum
effort and with bonus pay there were no additional effects
For other group, agents were choosing the minimum effort level, and
with bonus pay effort goes up to the same level as with own group
Why is there a maximum effort?
We do not observe crowding out, but this could still happen outside
the observed parameter values
Specifically, if we increased or decreased b enough, effort with respect
to own group could decrease
From the four points we observe, we cannot tell whether we are in a
crowding-out world

Conclusions
Hiring information-spreading agents has a positive effect on
knowledge about the scheme
The effect is driven by agents on incentive pay
Flat-pay agents not significantly different from no agent
In turn, increased knowledge about the scheme increases take-up
Shows that information costs can be important even in
developing-country contexts
Incentive pay works by increasing effort with respect to socially
distant households
Incentive pay does not change effort with respect to socially proximate
households
Incentive pay may overcome social barriers—in this specific context

More on the two-task model
Let Y1 and Y2 be the binary outcomes for the two tasks and e1 and
e2 the corresponding effort levels
0 < e < e < 1 define bounds for both e1 and e2
Let θ1 and θ2 denote the non-pecuniary pay-offs to the agent from
success in task 1 and 2, respectively
The principal receives the same pay-off π for both tasks

The agent’s problem
Agent’s cost of eﬀort:
c(e1, e2) =
1
2
c1e2
1 +
1
2
c2e2
2 + γe1e2
The parameter γ ≥ 0 can is a measure of the substitutability of task 1
and 2 in the cost of eﬀort
If c1 = c2 = γ = c and θ1 = θ2 = θ, then the setup collapses to the
single-task model
WLOG, assume c1 ≤ c2; as before, b = w − w
The agent maximises:
max
e1,e2
(θ1 + w)e1 + (θ2 + w)e2 + w(1 − e1) + w(1 − e2) − c(e1, e2)

Two-task solution
Solution when both eﬀort curves are internal:
˜e1 (b) =
(c2 − γ) b + c2θ1 − γθ2
c1c2 − γ2
˜e2 (b) =
(c1 − γ) b + c1θ2 − γθ1
c1c2 − γ2

Two-task solution
Define
ê1(b) =



θ1+b−γe
c1
if ˜e2(b) ≤ e
˜e1(b) if e < ˜e2(b) < e
θ1+b−γe
c1
if ˜e2(b) ≥ e
ê2(b) =



θ2+b−γe
c2
if ˜e1(b) ≤ e
˜e2(b) if e < ˜e1(b) < e
θ2+b−γe
c2
if ˜e1(b) ≥ e.
The complete second-best solution for the two-task model is given by:
e∗
1 (b) = max{min{ê1(b), e}, e}
e∗
2 (b) = max{min{ê2(b), e}, e}.

Crowding out
The second-order condition for a maximum requires
γ2
< c1c2
Allows for ‘crowding out’: bonus payment may crowd out intrinsic
motivation (Gneezy & Rustichini; B´enabou & Tirole; Frey)
Two main cases:
γ < c1 < c2 (relatively low substitutability): no crowding out
c1 < γ < c2 (the tasks are relatively substitutable in the cost of eﬀort):
crowding out when both curves are internal

The intrinsically preferred task
The ‘intrinsically preferred task’: The task in which the agent exerts a
greater eﬀort when there is no bonus pay.
Task 1 is the intrinsically preferred task iﬀ ˜e1 (0) > ˜e2 (0), or
θ1
c1 + γ
>
θ2
c2 + γ
Intuitively, task i is more likely to be intrinsically preferred if θi is
larger or ci smaller.

Raw scores by minisurvey

Enrolment by minisurvey

Knowledge test questions, survey wave 1
1 Does the programme cover the cost of treatment received while admitted to a hospital
(hospitalisation)?
Yes.
2 Does the programme cover the cost of treatment received while not admitted to a
hospital (out-patient treatment)?
No.
3 Who can join this programme?
Households designated as being Below the Poverty Line.(Those who said ‘the poor’, ‘low
income’ or similar were marked as correct.)
4 What is the maximal annual expenditure covered by the scheme?
30,000 rupees.
5 How much money do you have to pay to get enrolled in the scheme?
30 rupees per year.
6 How many members of a household can be a part of the scheme?
Up to ﬁve.
7 What is the allowance per visit towards transportation to the hospital that you are entitled
to under the RSBY scheme?
100 rupees. (This was the expected answer, although strictly speaking the transportation
allowance is subject to a maximum of 1000 rupees per year, i.e. ten visits.)
8 Is there an upper age limit for being covered by the scheme? If yes, what is it?
There is no upper age limit.

1 What is the maximum insurance cover provided by RSBY per annum?
30,000 rupees.
2 Does the beneﬁciary have to bear the cost of hospitalisation under the RSBY scheme up
to the maximum limit?
No.
3 Are pre-existing diseases covered under RSBY?
Yes.
4 Are out-patient services covered under RSBY?
No.
5 Are day surgeries covered under RSBY?
Yes.
6 Does the scheme cover post-hospitalisation charges? If yes, up to how many days?
Yes, up to 5 days. (Anyone who answered ‘yes’ was marked as correct.)
7 Are maternity beneﬁts covered?
Yes.
8 If a female RSBY member has given birth to a baby during the policy period, will the
baby be covered under RSBY?
Yes.

1 Under RSBY, how many family members can be enrolled in the scheme?
Five.
2 What is the maximum insurance cover provided by RSBY per policy period?
30,000 rupees.
3 If hospitalised, does an RSBY cardholder have to pay separately for his/her medicines?
No.
4 If hospitalised, does an RSBY cardholder have to pay separately for his/her diagnostic
tests?
No.
5 Is it compulsory for an RSBY cardholder to carry the smart card while visiting the hospital
for treatment?
Yes.
6 If an RSBY cardholder is examined by a doctor for a health problem but not admitted to
the hospital, will the treatment cost be covered under RSBY?
No.
7 What is the duration/tenure of the RSBY policy period?
1 year.
8 How can an RSBY cardholder check if a particular health condition is covered under
RSBY prior to visiting the hospital for treatment?
Multiple correct answers, see text.

Impact on enrolment
(1) (2) (3) (4)
Enrolled Enrolled Knowledge Enrolled
(OLS) (Reduced form) (First stage) (IV)
Knowledge 0.206*** 0.390***
(0.00910) (0.128)
Incentive-pay agent in village 0.0816** 0.209***
(0.0362) (0.0618)
Time fixed effects Yes Yes Yes Yes
Taluk fixed effects Yes Yes Yes Yes
Observations 5641 5641 5641 5641
[para,flushleft] Notes: Weighted least squares regressions. Each household is given the same weight, divided equally
between all observations of that household. Standard errors, in parentheses, are clustered at the village level. * p<0.10, **
p<0.05, *** p<0.01

Impact of knowledge on take-up
IV estimates suggest one SD increase in knowledge score increases
likelihood of enrolment by 39% points
IV estimates nearly double that of OLS
Potential explanation: LATE (average effect on ‘compliers’)
Possible concerns with IV:
Persuasion to enrol (but incentives not based on enrolment)
Endorsement effect (should not differ b/w incentive-pay and flat-pay
agents)
Strategic behaviour

Physical distance
(1) (2) (3)
Incentive pay 0.160* -0.128 -0.254
(0.0923) (0.139) (0.162)
Social distance -0.655*** -0.766***
(0.214) (0.249)
Incentive pay x social distance 0.794*** 0.867***
(0.217) (0.260)
Castes live apart -0.384**
(0.182)
Incentive pay x castes live apart 0.392*
(0.200)
Village size in thousands 0.196 0.205 0.288*
(0.154) (0.158) (0.158)
Agent and household characteristics Yes Yes Yes
Time and taluk fixed effects Yes Yes Yes
Social distance metric - Composite Composite
[para,flushleft]

Main results, Shimoga district only
(1) (2) (3)
Agent in village 0.210** 0.191**
(0.0823) (0.0743)
Flat-pay agent in village -0.0289
(0.121)
Incentive-pay agent in village 0.317***
(0.0683)
Time fixed effects No Yes Yes
Taluk fixed effects No Yes Yes
t-test: flat=incentivised (p-value) 0.007
[para,flushleft]
Notes: Weighted least squares regressions. Each household is given the same weight, divided equally between all observations of
that household. Standard errors, in parentheses, are clustered at the village level. * p<0.10, ** p<0.05, *** p<0.01

Treatment effect interacted with agent characteristics
(1) (2)
Knowledge Knowledge
Treatment (agent in village) 0.187*** -0.499
(0.0572) (0.331)
Treatment x agent is 30+ 0.0453
(0.0916)
Treatment x agent is 50+ -0.0826
(0.0938)
Treatment x agent of forward/dominant caste -0.102
(0.0894)
Treatment x agent household head has completed primary school -0.105
(0.0931)
Treatment x agent has ration card -0.0642
(0.123)
Treatment x agent owns her home 0.148
(0.108)
Treatment x agent is Self-Help Group president 0.00918
(0.0865)
Treatment x agent autonomy 0.121**
(0.0466)
Time fixed effects Yes Yes
Taluk fixed effects Yes Yes
Observations 5641 5641
[para,flushleft] Notes:
Weighted least squares regressions. Each household is given the same weight, dividedMaitreesh Ghatak (LSE) Motivating knowledge agents

Is the effect symmetric (or, does direction matter)?
Dominant-caste agent
Dominant Non-dominant Difference
household household
Flat pay 0.22 -0.17 0.39***
(0.1) (0.05) (0.11)
Incentive pay 0.12 0.11 0.01
(0.09) (0.04) (0.09)
Difference -0.1 0.28** -0.38***
(0.14) (0.06) (0.15)
Non-dominant-caste agent
Dominant Non-dominant Difference
household household
Flat pay -0.2 0.09 -0.29**
(0.09) (0.05) (0.11)
Incentive pay 0.08 0.17 -0.09
(0.08) (0.03) (0.08)
Difference 0.28** 0.08 0.2
(0.12) (0.06) (0.14)
[para,flushleft]

Discussion: The eﬀect size
On average, agents were paid a bonus of 8 rupees per ‘successful’
household
Increased raw score by 0.6 and take-up rate by 8 %-points
It appears that a modest amount of incentive pay can wipe out
knowledge gap between own and cross groups (part-time work)
Suggestive evidence that people spend on average 3–4 days full-time
on agent work in each round
400 rupees average pay / 4 days of work = 100 rupees per day
Corresponds to typical unskilled wage in the area
Agents may be more sensitive to having an incentive rather than level
of incentive (Filmer and Schady, 2009, Thornton, 2008, Banerjee et
al., 2010)

Extra material
More detail on the two-task model
Shimoga only
Impact on enrolment
The time dimension
Agent characteristics
Physical distance
Symmetry
Discussion: The eﬀect size
Knowledge test questions

04.24.2013 - Maitreesh Ghatak

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