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 enoughinformation to be able to benefit from a service

Information costs responsible for low take-up of welfare schemes indeveloped 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 spreadingawareness 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 boundariesMay explain heterogeneity in programme awareness

Interaction effects: Do incentives alleviate, or exacerbate, thepotentially 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 socialdistance

Run a randomised experiment in which we hire agents to spreadinformation 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 levelof 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 impacton knowledge transmission

Incentive pay seems to cancel out negative effect of social distance

. . . but incentive pay has no impact on knowledge transmission forsocially proximate agent-beneficiary pairs

No evidence of ‘crowding out’



Theoretical framework

Aim: A model to look at interactions of intrinsic preferences andincentive 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’seffort

Think of success as having the household’s knowledge about thegovernment scheme exceed a certain thresholdAlternatively, the task is successful if the household signs up for thescheme

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, solimited liability (no fines)

The agent’s disutility of effort is 12ce

2 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 ofπ (which may have a pecuniary as well as a non-pecuniarycomponent)

We assume that the principal’s pay-off incorporates the direct pay-offof the beneficiaries as well as how the rest of society values theirwelfare




Let w be the pay that the principal offers to the agent in the case ofsuccess, and w , the pay in the case of failure

Then b ≡ w − w can be interpreted as bonus pay with w as the fixedwage component

The agent’s objective is to maximise:

maxe

(θ + w)e + w(1− e)− 1

2ce2

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 differenttypes of households

Unlike the classic multi-tasking model, the outcomes associated withthe 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 sameconditional payments, w and w , for the two tasks

May be politically, socially, or legally constrained to offer the same payfor all tasksRelevant characteristics of the agent and/or tasks may not beobservable to principal (e.g. favourite students)



A solution without crowding out (relatively lowsubstitutability)

e

e

b

e1

e2



A solution with temporary satiation in e2 (intermediatesubstitutability)

e

e

b

e1

e2



A solution with crowding out (relatively highsubstitutability)

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 ofincentive pay

The intrinsically preferred task is not necessarily the one for whichintrinsic 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) increasesEffort in the easier task increases at least as much as effort in theharder taskIf the two tasks are relatively complementary: Effort in both tasksincreasesIf the two tasks are relatively substitutable: Effort in the easier taskincreases, 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 Indianpublic-private health insurance scheme for the poor called ‘RashtriyaSwasthya Bima Yojana’—henceforth, RSBY

Set in two districts in the south Indian state of Karnataka: Shimogaand Bangalore Rural

The scheme was launched in these districts in Feb–March 2010

Key features of programme:

Eligibility criterion: Below-Poverty-Line (BPL) designationCovers hospitalization expenses for 700 specified medical conditionsand proceduresAnnual expenditure cap of 30,000 rupees (600 USD) per householdPolicy 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 400rupees every three months (38 villages)Incentive-pay group: local agent recruited and paid a fixed amount of200 rupees, plus a bonus depending on the level of RSBY knowledgeamongst 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 localSelf-Help Group (SHG)

Agent’s task: spread information about RSBY among eligiblehouseholds



Experimental design

Average pay designed to equal 400 rupees across the two treatmentgroups

That is, the average bonus was 200 rupeesThe aim was to isolate the ‘incentive effect’ of the contract structurefrom the ‘income effect’

Payment structure revealed to agent after recruitment

The aim was to isolate the ‘incentive’ effect of contract structure frompotential ‘selection’ effect

Attrition could re-introduce selection bias, but no agent quit afterbeing told about the payment structure

Four agents quit a few months later, one due to pregnancy and threedue to migrationThose villages excluded from our analysisFinal 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 wereinterviewed 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 ofknowledge about RSBY (also used to pay agent)Measure take-up of RSBYCollect limited background information on households

Each knowledge test consisted of 8 questions relating to RSBY (score0–8)

Main outcome variable is the knowledge-test z-scores; we also look atenrolment 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 -0.058primary 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 0.11the 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’tivepay pay −Control −Control −Flat

Household is of forward/ 0.25 0.18 0.17 -0.070 -0.084∗ -0.015dominant 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 625Notes: 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 overlapWeighted 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 divisions4 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 YesObservations 5641 5641 5641t-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 importantdeteminant 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 absolutedifference in the agent’s and the household’s characteristics

Construct ‘composite’ social distance as the sum of the fourindividual 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 agentis 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 distantagent-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 Yeshousehold characteristicsTime and region Yes Yes Yes Yes Yes Yesfixed effectsObservations 2900 2900 2900 2900 2900 2900Social distance metric N/A Compo- Caste Educ- Ration Home

site only ation card ownershiponly 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 maximumeffort and with bonus pay there were no additional effects

For other group, agents were choosing the minimum effort level, andwith 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 outsidethe observed parameter values

Specifically, if we increased or decreased b enough, effort with respectto own group could decreaseFrom the four points we observe, we cannot tell whether we are in acrowding-out world



Conclusions

Hiring information-spreading agents has a positive effect onknowledge 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 indeveloping-country contexts

Incentive pay works by increasing effort with respect to sociallydistant households

Incentive pay does not change effort with respect to socially proximatehouseholds

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 ande2 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 fromsuccess 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

2c1e

21 +

1

2c2e

22 + γe1e2

The parameter γ ≥ 0 can is a measure of the substitutability of task 1and 2 in the cost of effort

If c1 = c2 = γ = c and θ1 = θ2 = θ, then the setup collapses to thesingle-task model

WLOG, assume c1 ≤ c2; as before, b = w − w

The agent maximises:

maxe1,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

e1(b) =

θ1+b−γe

c1if e2(b) ≤ e

e1(b) if e < e2(b) < eθ1+b−γe

c1if e2(b) ≥ e

e2(b) =

θ2+b−γe

c2if e1(b) ≤ e

e2(b) if e < e1(b) < eθ2+b−γe

c2if e1(b) ≥ e.

The complete second-best solution for the two-task model is given by:

e∗1 (b) = max{min{e1(b), e}, e}e∗2 (b) = max{min{e2(b), e}, e}.



Crowding out

The second-order condition for a maximum requires

γ2 < c1c2

Allows for ‘crowding out’: bonus payment may crowd out intrinsicmotivation (Gneezy & Rustichini; Benabou & Tirole; Frey)

Two main cases:

γ < c1 < c2 (relatively low substitutability): no crowding outc1 < 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 agreater 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 islarger 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 ahospital (out-patient treatment)?No.

3 Who can join this programme?Households designated as being Below the Poverty Line.(Those who said ‘the poor’, ‘lowincome’ 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 entitledto under the RSBY scheme?100 rupees. (This was the expected answer, although strictly speaking the transportationallowance 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 upto 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 thebaby 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 diagnostictests?No.

5 Is it compulsory for an RSBY cardholder to carry the smart card while visiting the hospitalfor treatment?Yes.

6 If an RSBY cardholder is examined by a doctor for a health problem but not admitted tothe 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 underRSBY 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 increaseslikelihood 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-payagents)Strategic behaviour



Physical distance


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

Observations 2900 2900 2327

[para,flushleft]



Main results, Shimoga district only


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

Observations 2885 2885 2885t-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 YesTaluk fixed effects Yes Yes

Observations 5641 5641

[para,flushleft] Notes:

Weighted least squares regressions. Each household is given the same weight, dividedequally 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



Is the effect symmetric (or, does direction matter)?

Dominant-caste agent

Dominant Non-dominant Differencehousehold 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 Differencehousehold 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 outknowledge gap between own and cross groups (part-time work)

Suggestive evidence that people spend on average 3–4 days full-timeon agent work in each round

400 rupees average pay / 4 days of work = 100 rupees per dayCorresponds to typical unskilled wage in the area

Agents may be more sensitive to having an incentive rather than levelof incentive (Filmer and Schady, 2009, Thornton, 2008, Banerjee etal., 2010)



Extra material

More detail on the two-task modelShimoga onlyImpact on enrolmentThe time dimensionAgent characteristicsPhysical distanceSymmetryDiscussion: The effect sizeKnowledge test questions


04.24.2013 - maitreesh ghatak

Business

programme knowledge

incentive pay

households knowledge

levelof knowledge

direct payoof

nonpecuniary payo

impact of incentive

social protection