Motivation

A challenge:

• Quality of service provision is low in many developing countries

• Recruiting and incentivizing service providers is difficult

A case: post Ebola epidemic in West Africa

• Caught many by surprise, no early warning
• Need for stronger decentralized bureaucracy
• 7/10 emerging infectious diseases spill over from animals

Motivation

• Local delivery models since 1990s: locally recruited actors, to deliver interventions or to do government tasks

• Popular, …. but is little known when and how they work

• Here: focus on agricultural extension workers

• Largely unpaid, using entrepreneurial worker model

• Agripreneur programs (World Bank, etc), but part of government system

Motivation

• Most people live in rural areas and crucially depend on agriculture (crops and livestock)
• Local animal welfare and disease risk go hand in hand
  • Sick animals are often eaten and cause health risk
  • Diseases are highly contagious
• Need for surveillance: case-reporting, contact tracing, vaccine tracking, data management, response

Motivation

Promising work on improving service provision:

1. Recruitment

2. Community monitoring

3. Financial incentives

We show:

• evidence 2 and 3 matter
• little movement on 1.
• we use a structural model to explore why, and when these might be more effective.

Past Work on Recruitment

Theoretically:

• Intrinsic motivation reduces shirking (Francois 2000)

Empirically:

• Lots of models rely on central selection by the state, where: quality increases with wage offers (Dal Bó, Finan, and Rossi 2013), Big Five personality traits (Callen et al. 2015), prosociality (Deserranno 2017) and at the margin with prospected career benefits (Ashraf, Bandiera, and Lee 2020)

Past Work on Recruitment

• Local agents may improve the targeting of and satisfaction with social programs, (Alatas et al. 2012; Basurto, Dupas, and Robinson 2020)

• Applicatons: environmental regulators (Duflo et al. 2018), agricultural extension officers (Dal Bó et al. 2021), microentrepreneurs (Hussam, Rigol, and Roth 2022), taxation (Balán et al. 2022)

• In fragile states: can local embedded agents act as complements to the state?

Past Work on Financial Incentives

By and large a success:

• Education: (Muralidharan and Sundararaman 2011), Kahn et al (2014), Glewwe et al (2010), De Ree et al (2010), (Leaver et al. 2021)

• Health status: Miller et al (2012), (Mohanan et al. 2021)

• Service delivery: Gertler and Vermeersch (2012), Desserano et al (2022)

• Service provider inputs: teachers (Duflo et al, 2012, Leaver et al. (2021)), nurse attendance (Banerjee and Duflo (2006))

Past Work on Community Monitoring

• View that bottom-up pressure by empowered citizens is key to improved service delivery

• Maybe even more effective when government can’t monitor behavior

• Effective in health if baseline is low: (Björkman and Svensson 2009; Christensen et al. 2021) vs (Raffler, Posner, and Parkerson 2024)

• Work in education is more mixed: Banerjee et al. (2010); Barr et al. (2012); (Pradhan et al. 2014); (Molina et al. 2016)

Monitoring: involving communities

• Original enthusiasm: “Power to the People” (Björkman and Svensson 2009)

  • How to improve service delivery and health outcomes in rural Uganda

• Intensive treatment

  • Assess quality of services provided in clinic/community-specific report cards

  • Disseminate report cards to community and to clinic staff

  • Develop joint action plans

  • Interface meetings between clinic staff and community

Monitoring: involving communities

• A small sample (n = 50)

• And amazing results:

  • Infant weights significantly higher

  • Under-5 mortality 33% lower

  • Immunization rates higher

  • Staff absenteeism and waiting times at clinics lower

Raffler et al (2024) replication …

• Same country, larger sample, teasing apart treatment components

Where does this leave us?

• Candidate explanation: differences as baseline

Policy question

1. Are performance-based payment schemes more or less effective than community monitoring mechanisms?
2. Are there interaction effects between recruitment strategies and incentive mechanisms?

• E.g. community monitoring may be less effective when service providers are selected by the state
• They may be more effective if selected by communities, trusted, knowledgeable, present

Study design

We study these questions in the context of a randomized agricultural intervention in Sierra Leone.

Core design elements (more on context to come)

Model {.smaller} (base model)

After Francois (2000). A worker has type \(\theta = (\eta,\mu)\) where:

• \(\eta \in \{ \underline{\eta} , 1 \}\) denotes embeddedness
  
• \(\mu \in \{ 0 ,\overline{\mu} \}\) denotes intrinsic motivation

Drawn according to:

So \(\rho\) governs the correlation between embeddedness and motivation: embedded people are possibly more effective, but are they more likely to be highly motivated?

Selection (\(T_s\)) and type

• Under state selection (\(T_s=0\)) the worker is a random draw from the candidate pool.
• Under villager selection (\(T_s=1\)), the worker is drawn from the pool with high embeddedness.

So state is more likely to select workers with high intrinsic motivation if and only if \(\rho < 0\).

Performance

• Workers choose effort \(e\)

• Performance depends on effort and embeddedness: \(\pi = e \eta\)

• Stochastic performance signal \(\sigma\), which is high (\(\sigma=1\)) with probability \(\pi\) or low (\(\sigma=0\)) with probability \(1-\pi\)

• Cost of effort: \(c(e) = \frac{e^2}{2\kappa}\) (alternative use of time, with wage elasticity of effort \(\kappa\))

Villager (Monitoring, \(T_m\))

• A representative villager selects a social sanctioning plan, which depends on the performance signal \(\sigma\):

\[s=(s(0),s(1)) \in [-T_m, T_m]^2.\]

• Interpretable as commitments (additional) to reward (\(s(\pi)<0\)) or punish (\(s(\pi)>0\)) performance.

• in line with Community Action Plan in experimental intervention (in CM)

Villager (Monitoring, \(T_m\))

• Punishment may be psychologically costly, according to \(\gamma\).

Payoff to villager:

\[u(\pi, s) = \pi - \gamma s.\]

• term \(\zeta\) captures village norms, impacts the probability that punishment is not costly for the villager

In crowding out version we have \(u(\pi, s) = \pi - s(\gamma - \xi T_p).\) (and replace \(s\) with \(-r\) to indicate rewards rather than sanctioning interpretation)

Timeline

The sequence of events is as follows:

1. Worker is selected by village (\(T_s=1\)) or state (\(T_s=0\)).
2. Villagers and worker learn whether the latter is subject to community monitoring (\(T_m=1\)) and/or P4P (\(T_p=1\)).
3. If \(T_m=1\), villager with guilt parameter \(\gamma\) commits to a conditional plan of social sanctioning \(\{s(\sigma)\}_{\sigma \in \{0,1\}}\).
4. The worker chooses effort \(e\).
5. The worker’s performance signal \(\sigma\) is realized.
6. Payoffs are realized and the game ends.

Results

• Sanctioning (\(T_m\)):
  • following a high signal: always praise the worker \(s(1)=-1\)
  • following a low signal: choose maximal shaming \(s(0)=1\) if guilt is low (\(\gamma=0\)) and minimal shaming \(s(0)=-1\) .
• Effort:

\[e(s^*;\gamma,\theta) = \begin{cases} \kappa \eta (\mu + T_p + 2 \phi \zeta T_m) & \mbox{if} \quad \gamma =0 \\ \kappa \eta (\mu + T_p) \quad & \mbox{otherwise}. \end{cases}\]

Context: Sierra Leone

• Poor service provision: mortality is 10% of live birthsqualk

• Threat of zoonosis is high and expected to increase

• Animals are essential for livelihoods (90% keep animals), comprising >50% of income

• Low information and poor/risky animal husbandry practices

  • ~50% have frequent animal diseases, with high mortality

• Animal diseases are highly contagious

• Large impacts on households and largely preventable

Context: study site

• In the wake of the Ebola epidemic, together with the GoSL, we implement a “One Health” intervention, addressing animal and human health

• 363 villages across 7 chiefdoms in Kono district

Cross randomized treatments

One Health Program

• Implemented from July 2017 to July 2019

• Recruitment of Community Animal Health Worker: CAHW

  • 21-day training, implemented by veterinarians

  • Tool kit and drugs for treatment of basic animal diseases

  • Creation One Health platform

• CAHW:

  • provides basic animal health services
  • demonstrates and provides information on best practices
  • reports suspected disease symptoms to MAF supervisors

One Health Program: 21-day training

Animal disease reporting form

One Health Program: Results

• From planner’s perspective it matters if we can improve CAHW performance:

  1. Recruitment

  2. Community monitoring

  3. Pay-for-performance

Recruitment

• Community selection: MAF facilitators hold village meetings to nominate and select the CAHW candidate

• “Business as usual”: CAHWs are recruited by state authorities in collaboration with Paramount Chiefs

• Eligibility requirements, ability tests, language, residence

• Training before assignment to incentives

Recruitment

• Key design element: double listing

  • Obtain nominees from both Paramount Chief and community
  • Allows us to have the counter-factual

Incentives

Community Monitoring

• CAHW and community develop joint action plans

• Interface meetings between ministry staff and community:

  • Two times: after 6 months and 1.5 years
  • CAHW performance revealed (relative and overall)
  • Allows community members to reward or impose social sanctions

Incentives

Pay-for-Performance

• CAHWs are paid based on their rank (across three groups)
• Two times: after 6 months and 1.5 years

Note: rank ensure revenue neutrality but introduces small risk of non-independence / collusion

Treatment Implementation

• Community monitoring and pay-for-performance implemented as designed

• The recruitment treatment not so much

  • Only in 100 villages do we have true double-listing (in many other cases either same person listed twice or no one listed by one party)

  • Implication: smaller sample and non-experimental interactions

Cases: Assignments

Small amount of attrition

Does community selection affect CAHW type?