The Plan Foncier Rural Impact Evaluation 2018 analyses and quantifies the impact of land tenure on productivity and food security disaggregated by target groups and gender. Land is an economic asset that serves multiple important purposes: residential, agricultural and communal (grazing lands, forests, water bodies, public infrastructure). Tenure security is crucial in ensuring poverty reduction, food security and equity. Farmers who lack secure land rights are less likely to carry out essential yield-improving investments in their land as the insecurity prevents them from committing to long-term plans.
The Promotion d'une Politique Foncière Responsable (ProPFR), is a GIZ funded programme to improve the land tenure security of households on customary land in the Borgou department of northern Benin.
The PFR activities to be evaluated at end-line consists mainly of demarcation and registration of land parcels (under customary tenure) as Titre Foncier or an Attestation de Droit Coutumière. The impact evaluation aims to quantify and analyse impact of these interventions on productivity and food security disaggregated by target groups and gender.
The research questions to be answered after the endline data collection are:
1) Do PFRs (or ADCs) contribute to a perception of greater land tenure security?
2) Does improved tenure security lean to a growth in agricultural investment and/or changes to management of land?
3) Do PFRs improve access to land and rights over land among marginalised groups (women, youth and migrants)?
4) Do PFRs lead to an increased number of land transactions?
5) Does increased land security address existing constraints on land markets and lead to more efficient allocation of land resources and thereby an increase in productivity?
6) Do property rights and improved user rights result in better access to credit, possibly allowing for income diversification and thus increasing household welfare?
7) Do the new arrangements put in place during the implementation of the PFRs facilitate the resolution of land conflicts, or even prevent the emergence of these land conflicts?
Kind of Data
Sample survey data [ssd]
Unit of Analysis
The Benin Plan Foncier Rural Impact Evaluation 2018 covers the following topics:
- Employment and enterprises
- Household assets
- Housing condition and sanitation
- Inventory of non-agricultural land
- Agricultural land
- Production in main season
- Utilization of chemical products
- Land given out
- Land sold
- Lost parcel
- Land perceptions
- Participation in PFR
- Food security
- Young (un-married) men
The clusters were spread across the communes of Bembéréké, Sinendé and Kalalé in the north and Tchaourou in the south of the department of Borgou.
Producers and sponsors
Daniel Ali Ayalew
The World Bank
The World Bank
The World Bank
German Agency for International Development
The World Bank
The impact evaluation consists of gender and youth disaggregated data collection at base line, before the start of the intervention, in both the treatment and control villages. End line data will be collected at least 2 growing seasons after issuing of documentation to farmers.
The sample consisted of 2968 households, which were taken from 26 villages selected for the implementation of a Plan Foncier Rural (PFR), or rural landholding plans, these were the treatment villages and 27 control villages that did not benefit from a PFR.
The treatment villages were assigned by the ProPFR team in geographic clusters. The assignment of control villages followed this geographic clustering, also using further village level data with the aim of finding similar villages to maximize comparability.
These clusters were spread across the communes of Bembéréké, Sinendé and Kalalé in the north and Tchaourou in the south of the department of Borgou.
Villages were selected from 11 geographical clusters of villages facing similar issues, allowing easier logistical planning for the rollout of the PFRs.
Villages selected to be part of the programme had the following characteristics:
• Bordering/near to a classified national forest
• At high risk of land grabbing,
• The presence of another GIZ supported SEWOH project1
• Agropastoral areas (in particular the presence of transhumance –cattle driving - corridors)
But should not have the following:
• Villages bordering Nigeria, within the band of increased security
• MCA intervention with a PFR
• Suffered serious conflict which could block the realisation of a PFR, or where a PFR may reignite past conflicts.
These characteristics alongside the desire of the implementing team to select villages in clusters, for practical reasons presented the first challenge in selecting suitable comparison villages to measure the impact of the ProPFR programme. Clustering meant that villages selected for comparison should be near the clusters to be comparable, but given the typical geography of villages in northern Benin, in that most people live in the village centre rather than spread evenly with sufficient density at the village boundary, and the lack of clearly defined village boundaries, a geographic discontinuity could not be exploited.
The second challenge in selecting comparison villages arose due to a change in the village definitions in 2013, when Benin changed from 3758 to 5290 villages which is often referred to as the “nouveau découpage”. Some old villages were split but there are no clearly defined village boundaries for the new set of villages. ProPFR selected from among the new villages, so the control villages also needed to be selected from this list. Given that the last census was collected prior to this new definition of villages, no data about the villages existed that could easily be used in matching villages to those selected for the ProPFR.
Due to this lack of data on the characteristics of the people residing in the villages, Geographical Information Systems (GIS) data were used to match each of the treatment PFR villages to a control village. Villages which were previously included in the MCA’s wave of PFRs were excluded from our study due to the difficulty in separating the effects of the two programs (MCA vs ProPFR).
For each PFR village, a buffer of 20km was drawn and the union constructed for each cluster. Within this area, other villages were considered as a potential control village. Of the selection criteria, the only one applicable from GIS data is the proximity to a national forest. Where villages were close to a national forest, we attempted to match it with a control village also close to a national forest.
The additional criteria on which villages were matched were the proximity to a main road (as classified by the Open Street Map shapefiles for roads) and the number of buildings in the central agglomeration of a village. Main roads are used as a proxy for access to markets and thereby potentially income levels.
The size of a village and the amount of land which can be used around it will be influenced by the size of the population as well as the presence of national forests.
This strategy is similar to a Coarsened Exact Matching (CEM) strategy (see Blackwell et al, 2009), in which key characteristics are reduced (perhaps from continuous variables) to a small number of categories and matched with one another exactly.
In our selection of villages, one control village was selected for each treatment village based on the key characteristics, defined as proximity to national forests (5km) and main roads (1km), and having a similar number of buildings (within 1km of the central point).
For a small number of villages, we faced an issue of common support, meaning there were no exact matches on the key characteristics. In this case other nearby villages were selected which fulfilled as many of these characteristics as possible.
Data were collected on a wide range of variables following the theory of change, which states that the improvements in institutions and the PFRs may lead to improved perceived land tenure security and improved access to land for women and young men through the activities carried out by the ProPFR team.
This perceived land tenure security is often seen as key to agricultural investments and thereby food security in the long term, as it allows long-term planning. The issuing of official documentation provides collateral for a loan should households wish to borrow and invest in productive activities or smooth consumption.
The response rate for PFRIE 2018 was of 98 percent.
Dates of Data Collection
Baseline data collection
Data Collection Mode
Data Collection Notes
The questionnaires were administered in face-to-face interviews in the respondents’ homes using tablets with Survey Solutions installed. Throughout the data collection, staff from C4ED checked the progress via the Survey Solutions online platform.
Checks of a subsample of entire surveys were made during the first two weeks to review the answers being entered by the enumerators, giving additional feedback to the INSAE team where issues were identified.
Institut National de la Statistique et de l'Analyse Économique
Republic of Benin
Center for Evaluation and Development
The Survey comprised two questionnaires namely:
1. Household Questionnaire:
Which comprised 14 modules with 7 rosters. Modules include household members, employment and enterprises, durable goods, housing, census of non-agricultural plots, agricultural plots, land donations, land sales, land losses, perceptions on land tenure, participation in PFR, loans, food security, young men and women.
2. Community (village) questionnaire:
The community survey was administrated to each village in the form of small group interviews to collect information on the socio-economic characteristics of these villages, local land tenure structures and practices, and local prices on agricultural inputs and production. The questionnaire was organized in 9 modules: characteristics of the survey participants, land tenure, land use, land market, land conflicts, other village structures and interventions, agriculture, PFR, and village chief. The characteristics of the participants were recorded in a separate roster.
The extensive household survey was first asked to the household head with additional modules to be answered by the wife of the household head (or the female household head) as well as a young male (defined as an unmarried man, aged 18-35).
Various consistency checks were performed to ensure data quality, including systematic reports of contradictory answers and of extreme values.
Throughout the data collection process, two main issues were reported. The first pertains to the sampling methodology of buildings, that led to the necessary replacement of pre-selected non-housing buildings. However, just short of 500 households required replacement. The majority of the buildings replaced were not residential buildings and were therefore not eligible for inclusion in the survey. These were replaced by the next building in the random order of buildings. The number of buildings for which nobody could be found for surveying was very low (23), thanks to the robust replacement protocol.
The second issue concerns the refusal of the village Sombouan 2 to participate in the survey. Despite several attempts, this village had to be excluded from the survey.
The data were also examined for missing information for required variables, and sections. Any problems found were then reported back to the supervisors where the correction was then made.
The World Bank
Daniel Ali Ayalew
The World Bank
Use of the dataset must be acknowledged using a citation which would include:
- the Identification of the Primary Investigator
- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
- the source and date of download
The World Bank. Plan Foncier Rural Impact Evaluation 2018. Ref. BEN_2018_PFRIE_v01_M. Dataset downloaded from [source] on [date].
DDI Document ID
Development Economics Data Group
The World Bank
Documentation of the DDI
Date of Metadata Production
DDI Document version
Version 01 (December 2020)