Targeting Incentives for Environmental Stewardship (TIES)

This study tests two types of Payment for Ecosystem Services (PES) contracts designed to reduce deforestation in rural Liberia. Communities are randomly assigned to one of three groups: (1) a control group that receives information only, (2) a standard PES group that receives payments conditional on forest conservation, and (3) a PES+ group that receives payments conditional on both forest conservation and accountable local governance. The intervention is implemented by Parley Liberia, a local NGO, and aims to promote environmental stewardship and equitable climate finance.

2025-11-03

2027-11-01

The study has two primary outcomes of interest:
1. High-bush Clearing: The extent of deforestation in designated no-clearing zones (i.e., “high bush” forest areas) within participating communities.
2. Inclusive Management & Accountability: The degree to which forest management and the distribution of PES contract payments are inclusive, transparent, and responsive to community needs.

High-bush clearing will be measured using satellite imagery and field inspections to detect forest loss, degradation, and fire activity in designated no-clearing zones.

Measures of inclusive management and accountability will be constructed using data from surveys administered to boundary farmers, non-boundary farming households, and community leaders. These measures will capture the extent to which community members are involved in decisions about forest use and payment distribution, and whether local leaders are perceived as responsive and trustworthy.

Secondary outcomes of interest include:
• Monitoring & Enforcement
• Biodiversity
• Other Clearing (Low-bush Farming)
• Material Welfare of Forest-dependent Households
• Welfare Spillovers [Material Welfare of non-Forest dependent Households]
• Equitable Distribution
• Satisfaction with Forest Management

• Monitoring & Enforcement: Will be assessed through survey questions about the existence of local rules governing forest use and farming, and whether those rules are actively enforced by community leaders or monitors.

• Biodiversity: Will be measured using environmental assessments and passive monitoring instruments such as camera traps or acoustic sensors to track species presence and habitat quality in a subset of communities.

• Other Clearing (Low-bush Farming) refers to land use outside the designated no-clearing zones. It will be measured using satellite imagery and field inspections to detect clearing activity in previously disturbed or secondary growth areas.

• Material Welfare of Forest-dependent Households: Will be constructed from survey modules that capture food security, asset ownership, consumption patterns, and informal transfers among households that rely on forestland for farming.

• Welfare Spillovers: Will be measured using the same indicators among non-forest-dependent households to assess whether the intervention affects broader community welfare beyond those directly impacted by conservation restrictions.

• Equitable Distribution: Will be evaluated through survey responses about how PES funds were allocated, including the amount invested or transferred, how benefits were distributed across households, and whether spending aligned with community priorities.

• Satisfaction with Forest Management: Will be measured through survey questions that capture perceptions of how forest clearing is managed and how PES funds were used, including whether community members feel the process was fair and effective.

This study uses a community-level randomized controlled trial (RCT) to evaluate the impact of two variants of a Payment for Ecosystem Services (PES) program in rural Liberia. A total of 190 eligible communities are randomly assigned to one of three groups: a control group (n = 70), a standard PES group (n = 60), and a PES+ group (n = 60). The PES group receives a contract that conditions payments on forest conservation, while the PES+ group receives a contract that includes both conservation and governance conditions. Randomization is blocked by geographic cluster and implemented using DeclareDesign in R. Baseline and endline surveys, environmental assessments, and remote sensing data will be used to measure outcomes.

Not available

Randomization will be conducted using the DeclareDesign package in R. Communities will be block-randomized by geographic cluster, with potential additional blocking on deforestation risk indicators collected at baseline.

The unit of randomization is the community. Each community includes a village and any attached hamlets, treated collectively as a single unit. All interventions—PES contracts, governance conditions, and control activities—are assigned at the community level. Surveys and environmental assessments are conducted with individuals within each randomized community, but no individual-level randomization is used.

Yes

190 communities

Approximately 4,940 survey observations: 2,470 individuals at baseline and 2,470 at endline, drawn from 190 communities in Lofa and Gbarpolu counties in Liberia. Each round includes 13 respondents per community—1 community leader, 6 boundary farmers, and 6 non-boundary households

190 communities will be randomly assigned to one of the following groups:
• Control Group: n = 70 communities
• Standard PES Group: n = 60 communities
• PES+ Group: n = 60 communities

The study will include 190 communities (70 control communities, 60 PES communities, and 60 PES+ communities). Preliminary power calculations indicate that this sample size will allow the detection of a 25-percentage point reduction in the probability of forest clearing at the community level with 80% statistical power, assuming no gains from covariate adjustment or blocking. We will likely have more power to detect effects on survey-derived outcomes, as we will typically have observations from multiple respondents within each community