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IMPAQ International, LLC Page i RLG Phase II - Data Quality Review Report

Millennium Challenge Corporation

MCA-Burkina Faso Rural Land Governance Phase II: Data Quality Review Report

Contract No: Task Order No. MCC-10-0111-CON-20 TO02

August 19, 2014

Submitted to:

Jack Molyneaux

Project Officer: Jennifer Witriol

Millennium Challenge Corporation 875 15th Street, NW

Washington, DC 20005

Submitted by:

Laurence Dessein

IMPAQ International, LLC 10420 Little Patuxent Parkway

Suite 300 Columbia, MD 21044 www.impaqint.com

http://www.impaqint.com/

IMPAQ International, LLC Page i RLG Phase II - Data Quality Review Report

TABLE OF CONTENTS

1. INTRODUCTION ..............................................................................................................................1

2. STUDY ZONES .................................................................................................................................4

3. HOUSEHOLD SURVEY ......................................................................................................................8

3.1 Sampling Frame ............................................................................................................................ 8

3.2 Household Sampling ..................................................................................................................... 9

3.3 Data Collection ............................................................................................................................ 10

3.4 Data Editing and Entry ................................................................................................................ 11

3.5 Survey Instruments ..................................................................................................................... 11

3.6 Distribution of Key Variables ...................................................................................................... 11

3.7 Data Issues Encountered............................................................................................................. 13

4. FIELD MANAGER SURVEY .............................................................................................................. 15

4.1 Sampling Frame .......................................................................................................................... 15




5. VILLAGE SURVEY .......................................................................................................................... 23

5.1 Sampling ...................................................................................................................................... 23




6. COMMUNE SURVEY ...................................................................................................................... 28

6.1 Number of Interviewed Individuals ............................................................................................ 28




7. CONCLUSION................................................................................................................................ 31

REFERENCES .................................................................................................................................... 32

APPENDIX 1: FINAL STATE OF DATA COLLECTION .............................................................................. 33

APPENDIX 2: BASIC DESCRIPTIVE STATISTICS .................................................................................... 43

1. Village Questionnaire ...................................................................................................................... 43

IMPAQ International, LLC Page ii RLG Phase II - Data Quality Review Report

2. Household Questionnaire ............................................................................................................... 48

2.1 Household Living Conditions ....................................................................................................... 48

2.2 Land Transfers ............................................................................................................................. 55

3. Field Manager Questionnaire ......................................................................................................... 59

3.1 Types of land fields ..................................................................................................................... 59

3.2 Perception of Land Security and land conflict on the fields ....................................................... 61

3.3 Commercialization of Agricultural Production............................................................................ 67

3.4 Perception of gender inequality in access to land ...................................................................... 69

APPENDIX 3: DATA QUALITY CHECKS ................................................................................................ 74

IMPAQ International, LLC Page iii RLG Phase II - Data Quality Review Report

TABLE OF EXHIBITS

Exhibit 1: Map of Burkina Faso Intervention Areas ...................................................................................... 5

Exhibit 2: List of RLG Phase I Treatment and Comparison Communes......................................................... 5

Exhibit 3: List of RLG Phase II Treatment and Comparison Communes ....................................................... 6

Exhibit 4: Map of Intervention and Comparison Communes ....................................................................... 7

Exhibit 5: Village Sampling Results................................................................................................................ 9

Exhibit 6: Sample of the Household Survey ................................................................................................ 10

Exhibit 7: Survey Modules ........................................................................................................................... 11

Exhibit 8: Household Characteristics .......................................................................................................... 12

Exhibit 9: Home Characteristics .................................................................................................................. 12

Exhibit 10: Percent of Households Owning Durable Goods ....................................................................... 12

Exhibit 11: Field Managers .......................................................................................................................... 13

Exhibit 12: Discrepancies Between 2006 Census and 2013 Enumeration .................................................. 14

Exhibit 13: Sample of the Field Manager Survey ........................................................................................ 16

Exhibit 14: Survey Modules......................................................................................................................... 16

Exhibit 15: Distribution of Field Managers by Age and Gender .................................................................. 17

Exhibit 16: Field Characteristics .................................................................................................................. 17

Exhibit 17: Percent of households that made investments and used inputs in the last 12 months .......... 18

Exhibit 18: Land Conflict: Experiences and Perceptions ............................................................................. 18

Exhibit 19: Percent of Households that Perceive the Following as Highly Problematic ............................. 19

Exhibit 20: Top 10 Agricultural Crops (Number of Fields) .......................................................................... 19

Exhibit 21: Total Household Agricultural Production (in Kg) ...................................................................... 20

Exhibit 22: Livestock in the last 12 Months (Number of Households): ....................................................... 20

Exhibit 23: Counts of Missing Values for Surface Areas ............................................................................. 22

Exhibit 24: Sample of the Village Survey ..................................................................................................... 23


Exhibit 26: Percent of Villages with Village Authorities and Institutions .................................................... 25

Exhibit 27: Total Number of Land Conflicts across All Authorities ............................................................. 25

Exhibit 28: Average Number of Land Disputes Presented to the Village Authorities ................................. 26

Exhibit 29: Average Number of Resolved Land Disputes by the Village Authorities .................................. 26

Exhibit 30: Sample of the Commune Survey ............................................................................................... 28


Exhibit 32: Commune Characteristics ......................................................................................................... 29

IMPAQ International, LLC Page 1 RLG Phase II - Data Quality Review Report

1. INTRODUCTION

This report reviews the quality of the Rural Land Governance Project Phase II baseline (pre-intervention) data collected by the Bureau d'Etudes et de Recherche pour le Développement (BERD) in Burkina Faso. The data collection was contracted by the Millennium Challenge Account – Burkina Faso (MCA-BF) for the evaluation of the Millennium Challenge Corporation’s Rural Land Governance Project in the country. In 2008, the government of Burkina Faso and the Millennium Challenge Corporation (MCC) signed a USD 480.9 million five-year compact over 2009–2014 to finance a poverty-reduction program in Burkina Faso. The overall objective of the Compact is to contribute to poverty reduction in the country via economic growth. The Compact includes the following four projects:

Agricultural Development Project,

Rural Land Governance Project,

Roads Rehabilitation Project and

Burkinabé Response to Improve Girls’ Chances to Succeed (BRIGHT II) Schools Project. The Rural Land Governance Project (RLG) was launched in September. The RLG represents a substantial investment in land issues and is structured around three components:

Legal and Procedural Change and Communication This Project Activity supported the Government’s efforts to improve rural land laws and the regulatory and procedural framework to implement those laws. Most notably, the Project played a key role in the development of Law No. 34/2009 “On Rural Land Tenure” and its implementing regulations in 2009-2010, and Law No. 34/2012 “On Agrarian and Land Reform in Burkina Faso.” These efforts were complemented by a public outreach program to inform people about the new legislation and its expected benefits.

This Activity was the first one implemented and set the framework for the other RLG activities, including decentralization of land administration and conflict resolution institutions, and issuance of rural land possession certificates (APFRs).

Institutional Development and Capacity Building This Project Activity, in conjunction with the Legal and Procedural Change and Communication Project Activity, worked to improve institutional capacity to deliver land services in rural areas. Most notably, this activity supported extensive training of GOBF officials from various ministries, and the establishment and operations of commune-level rural land services offices (SFRs), village level land commissions (CFVs) that support SFR operations, and village level conflict resolution commissions (CCFVs) that mediate land conflicts. Implementation took place at the commune and village level in the Project’s 17 Phase 1 municipalities


on a pilot basis, and expanded in 2013 to an additional 30 Phase 2 communes based on certain targets reached during Phase 1.

Site-Specific Land Tenure Interventions This Project Activity supported a variety of site-specific land rights formalization interventions. Activities included:

o Preparation of land titles and land leases for recipients of farmland in the new Di Irrigation Perimeter (the Perimeter was developed under the Agriculture Development Project) in 2014;

o Preparation of leases for users of land in existing irrigation perimeters near the Di Perimeter in 2014;

o Preparation of rural land possession certificates (APFRs) for non-irrigated land in the Project’s 47 implementation communes in 2013-2014;

o Provision of APFR-like certificates to households in Ganzourgou Province in 2010; and

o Working with local populations to develop participatory land and natural resource use plans.”

The RLG activities aim to make land legislation and administration procedures more user-friendly and accessible, to improve public land services in rural areas, and to facilitate participatory land-use management, registration of land rights, and conflict resolution. The expected results of the project are to improve land tenure security, reduce land conflicts, encourage productive land investments, and promote economic growth and reduce poverty in the intervention zones. The impact evaluation of the RLG project will address four research questions:

1. Do project activities lead to an improved perception of land security?

2. Is there a reduction in the number of land conflicts?

3. Does improved land tenure security act as an incentive for producers’ investments?

4. Does land tenure improve for vulnerable groups (for example, women)? The RLG project is being implemented in a phased approach consisting of a pilot phase (Phase I) and an extension phase (Phase II). Phase I provided the following activities in years 1 and 2: legal reform and institutional strengthening in 17 communes. These activities were followed by APFRs and other formalization activities in year 3. Phase II provided similar activities in 30 additional communes. It is important to note that in Phase II, there was no national level legislative assistance since the main legal changes were completed at the national level as part of Phase I.


The purpose of the data reviewed in this report was to establish a baseline for the RLG Phase II intervention. This baseline data was designed to be used subsequently for the impact evaluation. In September–December 2013, BERD conducted the following surveys:

household survey

field manager (parcel owner) survey

village survey

commune survey The impact analysis will incorporate information from all four surveys. In this review of the data, we review all four survey data sets. The focus of the report, however, is on the two main surveys:

1. the household survey and

2. the field manager survey.

These two surveys contain essential data necessary for the impact analysis, as our evaluation design consists of econometric models using individual and household level data. Since the two other surveys – the village survey and the commune survey – are less critical to the impact evaluation, we briefly described these surveys as well. For each survey, we present the sampling frame, sampling coverage, survey instruments, descriptive statistics of key variables and any data issues encountered. The rest of the report is organized as follows: 2) Study zones 3) Household survey 4) Field manager survey 5) Village survey 6) Commune survey and 7) Conclusion.


2. STUDY ZONES

Burkina Faso is located in a vast plateau south of the bend of the Niger River. Most of the country is flat and there is relatively low rainfall. Burkina Faso is home to an important hydrographic network which comprises three basins: the Volta, the Comoé and the Niger. The RLG Phase I was implemented in 17 communes and the RLG Phase II was implemented in 30 communes in various regions along these basins (See Exhibit 1 for the location of the communes). The 17 communes in Phase I (dark green in the map) and the 30 intervention communes in Phase II (light green in the map). Based on limited documentation and discussions with from MCA-BF, IMPAQ understands that the RLG intervention communes were were selected based on the following criteria:

Significant land tenure issues or disputes;

Located near or within the Agricultural Development Project (ADP) new perimeter;

Located in or near another irrigation perimeter;

Contains important infrastructure such as a livestock market;

Located near Compact road investments.

Furthermore, there was an effort to cluster communes, so that a concentration of several rural communes could facilitate the coordination of MCA programs.1 In Phase I, the 17 treatment and 17 comparison communes were selectd by MCA-BF prior to IMPAQ’s involvement in the project. In Phase II, 30 tretament communes were selected by MCA-DF based on the above criteria. IMPAQ collaborated with MCA-BF to select 29 comparison communes for Phase II. A list of the intervention and comparison areas for Phase I is presented in Exhibit 2 and for Phase II in Exhibit 3. The location of the intervention and comparison communes is presented in Exhibit 4. The comparison communes in Phase II were collaboratively selected by IMPAQ and MCA-BF using a purposive rather than a probabilistic sampling procedure. For each treatment commune, the team identified a comparison commune that satisfied the following criteria:

It is located in the same province as the treatment commune (assuming that location in the same province means sharing similar administrative structures).

It is located in a zone aménagée (irrigation infrastructure) and is relatively similar in size to the intervention commune.

It has a similar presence of gold mines, as in the treatment commune.

It is geographically close to the treatment commune.

1 See document MCA proposal for final selection of CRs oct 2007_vOct16.doc.


No other land-related interventions are being implemented in the commune.

It has a similar population size as the treatment commune (based on 2006 Census data).

It has a similar socio-rural area (based on documentation received from MCA-BF).2

Exhibit 1: Map of Burkina Faso Intervention Areas

Exhibit 2: List of RLG Phase I Treatment and Comparison Communes

Treatment Comparison

BAMA BEREGADOUGOU BANFORA BIEHA BOUDRY BOMBOROKUY DI BOUROUM DJIBO DANDE GUIBA GAONGO KAMPTI GOURCY KONGOUSSI KOMPIENGA LEO MEGUET

2 According to Cartographie des Zones Socio-Rurales, AgWater Solutions report (2010), Burkina Faso can be divided into 16 zones that are homogenous in terms of climate conditions, natural resources and other socio-economic characteristics (e.g. access to markets and main economic activities).



LOUMBILA NOBERE MOGTEDO PABRE OUAHIGOUYA RAMONGO OUARGAYE SABCE PAMA TONGOMAYEL SABOU YE SONO YONDE ZAM ZOUNGOU Source: RLG Phase I Baseline Survey, 2010

Exhibit 3: List of RLG Phase II Treatment and Comparison Communes


N° Commune Province N° Commune Province

1 Gassan Nayala 1 Kougni Nayala

2 Moussoudougou Comoé 2 Samogohiri Kénédougou

3 NiangoloKo Comoé 3 Soubakaniedougou Comoé

4 Sideradougou Comoé 4 Mangodara Comoé

5 Douna Leraba 5 Dakoro Leraba

6 Koubri Kadiogo 6 Pabre Kadiogo

7 Saaba Kadiogo 7 Komsilga Kadiogo

8 Bittou Boulogou 8 Zabre Boulogou

9 Lalgaye Koupelogo 9 Komin-yanga Koupelogo

10 Zimtenga Bam 10 Nasséré Bam

11 Kokologho Boulkimiende 11 Nanoro Boulkimiende

12 Poa Boulkimiende 12 Kindi Boulkimiende

13 Tenado Sangie 13 Pouni Sanguie

14 Cassou Ziro 14 Bakata Ziro

15 Bere Zoundwego 15 Gomboussougou Zoundwego

16 Binde Zoundwego 16 Gogo Zoundwego

17 Tansarga Tapoa 17 Tampaga Tapoa

18 Padema Houet 18 Satiri Houet

19 Samorouguan Kenedougou 19 Sindo Kenedougou

20 Lanfiera Sorou 20 Yaba Nayala

21 Kassoum Sorou

22 Sapouy Ziro 21 Guiaro Nahouri

23 Banzon Kenedougou 22 Kourouma Kenedougou

24 Tangaye Yatenga 23 Thiou Yatenga

25 Pobe Mengao Soum 24 Baraboulé Soum

26 Bourasso Kossi 25 Dokui Kossi

27 Didyr Sanguié 26 Godyr Sanguié

28 Toussiana Houet 27 Péni Houet

29 Djigouè Poni 28 Gbomblora Poni

30 Rollo Bam 29 Tikare Bam


Exhibit 4: Map of Intervention and Comparison Communes


3. HOUSEHOLD SURVEY

The household survey provides information on housing, possession of durables, land transfers and major household events. This survey also provides an enumeration of all household members who manage fields or plots of land necessary for the next survey. The household survey data was collected in both the intervention and comparison zones. To understand the sampling for the survey, it is important to keep in mind the evaluation design IMPAQ developed. IMPAQ plans to implement a difference-in-differences (DD) methodology. Based on this method, we have determined the necessary sample size and how to draw the households in the sample from the population of interest. The evaluation design can be summarized as follows:

1. The treatment group includes a sample of households located in villages from intervention communes where the RLG Phase II program has been rolled out.

2. The comparison group includes a sample of households located in villages from comparison communes where the RLG Phase II program has not been implemented.

3. Applying DD methodology, we will compare the changes in outcomes over time between the treatment and comparison groups.

Additional details on the sampling frame, sampling and data collection are presented in the following sections.

3.1 Sampling Frame Given the finalized study zones (30 treatment and 29 comparison communes in 20 provinces and 12 regions), the IMPAQ team performed power analysis to determine the necessary sample size – the total number of villages within each commune and the total number of households within each village to be sampled. We used two-stage sampling to account for the household clustering in villages – first-stage is sampling at the village level and second-stage is sampling at the household level. Our power analysis calculations indicated that a total of 4,000 households will be a sufficient sample to detect a 12% change in agricultural income.3 At 8 households per village, this required approximately 250 villages in treatment communes and 250 villages in comparison communes. IMPAQ used the most recent census of the population in Burkina Faso (2006) to obtain the list of villages in each commune and the total number of households in each village. From the compiled list of villages, the IMPAQ team then drew a group of villages to be sampled. 3 We calculated the minimum sample size to be 2,000 treatment and 2,000 comparison households under the assumption of 80% power and 5% significance level. Income data come from the PNGT2, a rural household survey conducted in Burkina in 2005.


Within each commune, IMPAQ sampled villages with probability proportional to size of the village (i.e., the household population of the village). From a total of 623 villages in treatment areas, 178 were selected to be sampled (See Exhibit 5). In the comparison areas, 179 out of 672 villages were selected to be sampled. The total number of villages in the sample is 357 in 59 communes.

Exhibit 5: Village Sampling Results

Treatment Comparison Total

Total number of villages 623 672 1,295

Number of villages sampled 178 179 357

Total number of sampled households 2,008 2,008 4,016

Since we used probability proportional to size sampling techniques with replacement, some villages were selected more than once into the sample (48 out of the 178 treated villages were sampled more than once).4 Each time a village was drawn into the sample, we selected 8 households. Thus, for example, Yorgo village in Bere commune was drawn twice. Therefore, we sampled and interviewed 16 households in Yorgo.

3.2 Household Sampling

IMPAQ supplied BERD with a list of treatment and comparison villages with the associated number of households to be sampled within each selected village. In the field, BERD enumerators traveled from village to village and from neighborhood to neighborhood to conduct an exhaustive enumeration of all households in the sampled villages. The enumeration data was collected to serve two purposes:

(i) to facilitate the random draw of the necessary number of households within each village from an enumeration list of all households and

(ii) to allow the calculation of population weights in case there were big population discrepancies between 2006 and 2013.5

With enumeration lists at hand, BERD data collectors randomly selected the designated number of households for each village. A total of 4,016 households (2,008 treatment and 2,008 control) were interviewed, thus reaching the target number of households (See Exhibit 6). There are a total of 32,595 household members across the total sample of 4,016 households. Among all household members, 8,096 were identified as field managers (4,076

4 Consistent with probability proportional to size sampling, the sampled villages with larger populations have a higher probability of being drawn. The sample of households in these larger villages was determined to be a multiple of 8. 5 IMPAQ has not received the enumeration data. The data, if of high quality, would provide up-to-date population figures in the sampled villages that would allow us to construct population weights.


treatment and 4,020 control).6 See Appendix 1 for summary results of the data collection by village and communes.

Exhibit 6: Sample of the Household Survey


Communes 30 29 59

Villages 178 179 357

Households 2,008 2,008 4,016

Household members 16,620 15,974 32,594

Plot managers 4,076 4,020 8,096

3.3 Data Collection The data collection was conducted in September and October of 2013 by 50 teams of enumerators hired by BERD. Each team was composed of 4 enumerators and 1 controller. Eleven supervisors oversaw 11 groups of teams organized by zones of communes. MCA provided some additional supervision. All enumerators, controllers and supervisors were trained by BERD prior to the implementation of the survey (September 2-16, 2013). The training included plenary and small group work sessions each training day. In addition, there was a debriefing session at the beginning and end of each training day. IMPAQ observed the training which was very effective. However, there were some logistic and organizational difficulties in implementing the survey. For example,

local officials were not informed of the upcoming survey in their community and, as a result, there were issues in obtaining timely access to respondents;

there was a shortage of GPS devices; and there were some difficulties in reaching respondents who lived in distant hamlets

outside the villages. IMPAQ identified these issues and suggested corrective actions to MCA-BF and the survey firm. It appears that survey firm took some corrective issues (e.g., provided enumerators with more GPS units), but some of issues persisted (reaching respondents in distant hamlets). A review of the difficulties encountered in the field by IMPAQ suggests that while these issues can present problems for data quality, in general, they affect only a small proportion of the

6 Field managers are members of the household who make most of the decisions concerning crops/trees to plant,

input use and the planning of activities related to crop/livestock/pasture, the commercialization of agricultural production/products derived from animals, and investment in the field. For commercial or residential fields, this includes commercial and family decisions. The field manager can be the owner of the field or only have rights to use it. There may be more than one field manager per household.


sample. As a result, we are reasonably confident that the collected survey data is adequate for the impact evaluation.

3.4 Data Editing and Entry

Field supervisors checked the questionnaires before the data were sent to the survey firm for data entry. Data entry was then conducted by BERD. After receiving the original data set from MCA-BF on March 17, 2014 IMPAQ began a data quality review. On May 8, 2014, IMPAQ received from MCA the revised by BERD data sets and proceeded to review the data quality of these files.

3.5 Survey Instruments

The household survey used a questionnaire composed of seven modules, each focused on a different area of interest (See Exhibit 7). Importantly, the household survey was used to enumerate all household members who manage fields that will be surveyed in the Field manager survey.

Exhibit 7: Survey Modules

1. Identification of the household

2. Housing characteristics

3. Land transfers

4. Characteristics of household members

5. Durable goods

6. Characteristics of field managers

7. Household events during last 24 months

3.6 Distribution of Key Variables

Exhibit 8 presents key household characteristics. Among all households in the sample, 7.3% were headed by women and 70.5% had an illiterate head of household. On average, households included 8.1 members.


Exhibit 8: Household Characteristics


Percent of households with female heads 6.1% 8.4% 7.3%

Percent of households with illiterate heads 68.9% 72.1% 70.5%

Average household size 8.0 8.3 8.1

Less than a third (31.9%) of all households lived in their current home between 0 and 5 years, while over two-thirds (68.1%) have been at the same home 6 years or more (See Exhibit 9). On average, there were 1.7 rooms per household.

Exhibit 9: Home Characteristics


Percent of households living 0–5 years in current home 32.4% 31.3% 31.9%

Percent of households living 6 or more years in current home

67.6% 68.7% 68.1%

Average number of rooms per home 1.7 1.8 1.7

Exhibit 10 presents the percent of households that owned durable goods: bicycles (91.8%), mobile or landline phones (82.6%), radios (55.2%), televisions (9.5%) and fans (2.3%).

Exhibit 10: Percent of Households Owning Durable Goods


Bicycle 91.8% 91.8% 91.8%

Phone 84.0% 81.2% 82.6%

Radio 57.1% 53.3% 55.2%

TV 11.4% 7.6% 9.5%

Fan 2.9% 1.7% 2.3%

There were a total of 8,096 field managers (i.e., household members who managed a field) identified. Specifically, there were 4,076 field managers in the treatment group and 4,020 in comparison group (See Exhibit 11). On average, there were two field managers per household.


Exhibit 11: Field Managers


Total number of field managers 4,076 4,020 8,096

Average number of managers per household 2.0 2.0 2.0

Percent of households with land transfer in last 2 years

4.6% 4.9% 4.7%

As indicated in Exhibit 10, few households (less than 5%) experienced a land transfer in the last two years.

3.7 Data Issues Encountered

In the data quality review, we did not identify any major data issues that would affect the overall quality of the analysis. We found the overall quality of the household survey to be adequate and we believe the data are suitable for the impact evaluation analysis. In the rest of this section, we list a few issues that are worth mentioning. There were occasional gaps in the total number of households from the enumeration relative to the latest 2006 General Population Census. During the enumeration phase, BERD data collectors documented differences in household sizes between the 2006 Census and the enumeration conducted in 2013. For the most part, the changes in population are related to natural population growth during these years. Some of these differences may also be due to the use of a slightly different household definitions. This resulted in enumerated households being smaller and more numerous. The implication of the this discrepancy for the impact analysis is that the our sample design was based on data from the 2006 census. If these numbers are no longer valid, then the sample selection may be affected. While it may not be surprising to observe changes in population over a seven year period, BERD looked into some of the discrepancies and identified reasons for the changes. Some of the changes between 2006 and 2013 are listed in Exhibit 12 with BERD’s assessment of the changes are presented following the exhibit. 7

7 Rapport Technique sur les Activités de Terrain: Enquête de base sur les ménages et l’agriculture. BERD. January

2014.


Exhibit 12: Discrepancies Between 2006 Census and 2013 Enumeration

Village Commune 2006 Census 2013 Enumeration

Rimini Niangologo 504 887

Nianaba Toussiana 527 376

Wempeall Toussiana 121 81

Didri Koubri 601 539

In the village of Rimini, Niangologo commune, 887 households were enumerated in 2013 against 504 in the 2006 Census. The reason for this remarkable increase was attributed to an influx of new households in the locality after the discovery of a vein of gold that turned the village into a mining site.

In the villages of Nianaba and WempeaII, Toussiana commune, household populations decreased respectively from 527 and 121 (during the 2006 Census) to 376 and 81 households (during the enumeration survey of 2013). According to the authorities of Toussiana, the 2006 count included displaced populations from Côte d’Ivoire who returned to their country after the end of the civil war.

In the village of Didri, Koubri commune, 539 households were listed in 2013 as compared to 601 in 2006. According to the village chef and the Village Development Council (Conseil Villageois de Développement, CVD), this is attributed to the fact that the population of the Kuinig-Banka section is now claimed to belong to a neighboring village.

Despite these observed changes in population from 2006, the original sampling design was maintained and BERD selected and interviewed the appropriate number of households in each village. BERD enumerators selected a random sample of households in each selected village and successfully collected data from almost 100% of the selected households. In summary, while there were minor issues in the survey implementation, the issues encountered in the field seem not significant. IMPAQ believes that the issues identified above should not have a detrimental effect on the data quality and its usability for the impact evaluation.


4. FIELD MANAGER SURVEY

Following the household survey, BERD enumerators proceeded with an individual questionnaire to all household members who were identified as field managers. The administration of this survey served two purposes:

1. to collect detailed information about each field or plot of land used by households and

2. to collect detailed information about the plot managers who make the decisions about these plots.

4.1 Sampling Frame

For all households in the sample, BERD interviewers established two enumerations:

(1) an enumeration of all fields and (2) an enumeration of all field managers.

Boxes 1 and 2 provide the definitions used in both enumerations.

Field definition: A field is defined as a piece of land owned by one person or a group of members of a family. A field can be enclosed by natural borders and can contain one or more parcels. The natural boundary of a field can be a road, a river, or a field belonging to someone else.

Field manager definition: A field manager is a member of the household who takes most of the decisions concerning crops/trees to plant, use of agricultural inputs, the planning of activities related to crop/livestock/pasture, the commercialization of agricultural production and products derived from livestock, and decisions concerning investments in the field. For fields that are used for commercial or residential purposes, this also includes decisions related to commercial and household activities. The field manager can be the owner of the field or have only user rights on it. For fields that are rented out to members outside the household, even if the tenant decides how to use the land, the field manager is the household member who owns the field or the member who decides about the appointment of a tenant. Therefore, the field manager is typically the person best placed to provide information on this particular field. For fields used as primary residence of the household, the field manager can be the head of household or another member of the household who is regarded as being responsible for this field.


The household survey identified 8,096 field managers (4,076 treatment and 4,020 control). They managed 16,370 land fields (8,422 treatment and 7,948 control). A field manager can manage multiple fields (See Exhibit 13).

Exhibit 13: Sample of the Field Manager Survey


Households 2,008 2,008 4,016

Household members 16,620 15,974 32,594

Field managers 4,076 4,020 8,096

Fields 8,422 7,948 16,370


The field manager survey used a questionnaire composed of thirteen modules, each focused on a different area of interest (See Exhibit 14).


1. Identification of fields

2. Ownership and acquisition of fields

3. Land rights of use

4. Field investments and use of inputs

5. Revenues from fields used for commercial purposes

6. Perceptions of and actual land conflicts

7. Agricultural production

8. Commercialization of agricultural production

9. Livestock

10. Employment

11. Transfers, rents and other revenues during last 12 months

12. General perceptions of land security

13. Gender inequality: perceptions of land access



There is an approximately equal distribution of male and female field managers – 4,073 men vs. 4,023 women (See Exhibit 15). However, female field managers tend to be younger, with 32% of them aged 30 or less. This percentage is only 19% among male field managers. Similarly, among those aged 65 and above, only 5% are women vs. 12% men.

Exhibit 15: Distribution of Field Managers by Age and Gender

Age range

Gender

Male Female Total

N Percentage N Percentage N Percentage

Younger than 30 765 19% 1,280 32% 2,045 25%

30–64 2,835 70% 2,541 63% 5,376 66%

Older than 64 473 12% 202 5% 675 8%

Total 4,073 100% 4,023 100% 8,096 100%

Exhibit 16 presents field characteristics for the households. There are a total of 16,370 enumerated fields among all households: 8,422 in treatment and 7,948 in comparison groups. Households reported four fields on average, with some owned and others in use. For the most part, households own at least one of the fields they reported (88.6%), while less than half of the surveyed use fields that belong to others (41.4%).

Exhibit 16: Field Characteristics


Total number of fields 8,422 7,948 16,370

Average number of fields per household 4.2 4.0 4.1

Percent of households who own at least 1 field 87.2% 90.0% 88.6%

Percent of households who use fields of others 43.7% 39.0% 41.4%

Exhibit 17 represents the percentage of households who made field-related investments and used agricultural inputs in the past 12 months. The questionnaire asked about whether the household has made certain investments and if not why. For illustrative purposes, we list five reasons (out of 14 surveyed): to build a new construction (27.0%), to plant permanent crops (29.1%), to buy a tractor (1.0%), to use fertilizer (71.9%) and to use seeds (68.5%).


Exhibit 17: Percent of households that made investments and used inputs in the last 12 months


To build new constructions 27.0% 27.1% 27.0%

To plant permanent crops (trees) 28.8% 29.4% 29.1%

To buy a tractor 1.3% 0.8% 1.0%

To use fertilizer 71.4% 72.4% 71.9%

To use seeds 68.5% 68.6% 68.5%

In 7.9% of households, at least on field manager reported that they or another member of the household has ever experienced a land conflict, with an average number of 2.8 conflicts per household (See Exhibit 18). Similar proportions of treatment and comparison households experienced land conflicts (7.6% treatment and 8.2% comparison); however, the average number of conflicts for treatment households was 3.3 and the average for comparison households was 2.2. This discrepancy in average number of conflicts per household suggests that the treatment areas were prone to more land conflicts than the comparison areas. At this stage, we do not have an explanation for the observed difference in land conflicts. We will investigate the discrepancy more thoroughly in the analysis. IMPAQ does not believe that this discrepancy will have a significantly affect the impact analysis since our analysis will control for initial condition in the treatment and comparison areas.

Exhibit 18: Land Conflict: Experiences and Perceptions


Percent of households experiencing land conflict 7.6% 8.2% 7.9%

Average number of conflicts per household 3.3 2.2 2.8

Percent of households fearing future conflicts 45.1% 43.1% 44.1%

Respondents also indicated the factors that may lead to land conflicts. In Exhibit 19, we present some of the most frequent reasons that were cited by respondents as highly problematic. We report percent of households where at least one field manager perceive these to be of most concern, in general:

the arrival in the village of outsiders looking for farmland (55.9% of households) the delay and cost to obtain legal property rights (55.1% of households), and


land disputes in their village (60.7% of households).8

There does not appear to be significant differences between the treatment and comparison group responses.

Exhibit 19: Percent of Households that Perceive the Following as Highly Problematic


Arrival of outsiders looking for farmland 57.9% 53.8% 55.9%

Delay and cost to obtain property rights 53.6% 56.7% 55.1%

Land disputes in their village 63.4% 58.1% 60.7%

Respondents were also asked about the crops that they planted in their fields. Respondents were specifically asked about 32 different types of crops. As indicated in Exhibit 20, sorghum was planted in 2,040 treatment fields and 2,356 comparison fields. Note households can plant the same crop in multiple fields. Exhibit 19 lists the top 10 crops that were planted by households.

Exhibit 20: Top 10 Agricultural Crops (Number of Fields)


1. Sorghum 2,040 2,356 4,396

2. Millet 2,017 1,769 3,786

3. Maize 1,658 1,582 3,240

4. Peanuts 1,537 1,524 3,061

5. Cowpeas 1,445 1,595 3,040

6. Sesame 663 733 1,396

7. Rice 540 717 1,257

8. Cotton 587 543 1,130

9. Gombos 502 556 1,058

10. Groundnuts 419 322 741

8 Field managers were asked whether, in general, land disputes were are a major problem, minor problem, or not a problem at all in their village.


In Exhibit 21, we present the quantity amounts and standard deviation of total household production during the past 12 months of the three mostly planted crops. Among households who reported agricultural production, the mean total household production in the treatment area was 1,043 kg for sorghum, 937 kg for millet and 2,113 kg for maize. Note that not all households grow these cereals (1,127 out of 2,008 households in the treatment area grow sorghum, for example). In the comparison area, the average total household production was of similar magnitudes – 1,126 kg for sorghum, 936 kg for millet and 2,260 kg for maize.

Exhibit 21: Total Household Agricultural Production (in Kg)


Crop Total household production

(kg) N

Total household production (kg)

N

Sorghum 1,043 (1,892) 1,127 1,126 (2,414) 1,195

Millet 937 (1,627) 1,000 936 (1,116) 917

Maize 2,113 (3,819) 1,215 2,260 (10,326) 1,199

In addition to questions on crops, the questionnaire also asked about livestock. As indicated in Exhibit 22, a total of 3,337 households in the sample raised some livestock during the last 12 months. Of those households that raised livestock, chickens were the most prevalent (2,943) followed by sheep and goats (2,804). A relatively small number of households raised pigs (657). The distribution of type of animal raised is similar for treatment and comparison households.

Exhibit 22: Livestock in the last 12 Months (Number of Households):


All livestock 1,663 1,674 3,337

Chickens 1,481 1,462 2,943

Sheep and goats 1,383 1,421 2,804

Cows 979 1,087 2,066

Pigs 274 383 657


The data quality review did not identify any major data issues that would affect the overall quality of the analysis. We find that the overall quality of the field manager survey is good and


we believe this data to be suitable for an impact evaluation analysis. However, there were some issues that were identified, investigated, and corrected by BERD. Below, we describe these issues and how BERD corrected the problems identified. BERD’s technical report9 of field activities identified two issues in the field:

i) underreporting of female field managers, and ii) enumerators’ understanding of the definition of ‘field manager’.

Specifically, data collection controllers noticed that, in some villages, households systematically underreported female field managers while in others villages controllers believed that certain household member should be listed as field managers. Following this discovery, BERD enumerators re-visited the problem villages to address the issues that were identified:

Underreporting. In total, 175 households in 27 villages were identified as possibly underreporting female managers and required verification. To correct the problem, eight investigators returned to the field from 19–26 November 2013, in order to review the data accuracy of these problematic surveys and confirmed that 75 households were indeed without female field managers. Following the review, BERD sent eight controllers back into the field from 20–26 December 2013 to re-administer the questionable surveys to the remaining 100 households to collect data from their female field managers. The corrected surveys were then entered into the analysis file.

Understanding. In some villages, BERD identified an issue with interviewers’

understanding of the definition of ‘field manage’ based on the low number of female field managers. This issue was discovered in 10 villages (two in Tangaye and eight in Thiou) and involved a total of 120 households. To correct this problem, two teams (2 controllers, 8 investigators) were deployed to the Thiou and Tangaye communes for a new data collection. This new data collection took place from 19–28 November, 2013. The corrected surveys were then entered into the analysis file.

In addition to the two main issues identified by BERD, IMPAQ identified some minor issues in the files. For example, 8,474 out of 16,376 (51.8%) did not provide values for their field surface size (see Exhibit 23). However, respondents who were not able to self-report the exact field surface area were asked to report the approximate size in hectare intervals. Nearly all respondents were able to provide an interval response to the surface area question. In response to the surface area used for various agricultural products, the same issue was encountered. That is, when field managers were asked to report the exact surface area that

9 Rapport Technique sur les Activités de Terrain: Enquête de base sur les ménages et l’agriculture. BERD. January 2014.


was used for various crops, 13,391 out of 24,826 (53.9%) responses had missing values. When asked to respond in intervals, nearly all field managers were able to respond (only 2.0% missing). Not having exact field and area sizes will prevent computing exact crop yields. However, we believe that using the approximate areas will allow the computation of reasonable accurate crop yield ranges.

Exhibit 23: Counts of Missing Values for Surface Areas

Section H: Identification of the Fields Section N: Agricultural Production

Area in Ha (values)

N % Area in Ha

(values) N %

Missing 8,474 51.8 Missing 13,391 53.9

Total 16,376 100 Total 24,826 100

Area in Ha (intervals)

N % Area in Ha (intervals)

N %

Missing 13 0.2 Missing 266 2.0

Total 8,474 100 Total 13,443 100

Despite the issues identified, we believe the data collected by BERD is of reasonable quality and suitable for the impact analysis. BERD took the initiative to identify and correct the problem. Once correct data was gathered, BERD entered the corrected data into the analysis file. As a result of BERD’s actions, we believe that the final data set is suitable for the impact analysis.


5. VILLAGE SURVEY

The purpose of the village survey was to collect information on population, infrastructure, land use and ownership arrangements, as well as on prices of agricultural and livestock products, and perceptions of tenure security at the village level.

5.1 Sampling

This survey covers the same villages as those selected for the survey of households (178 treatment and 179 control villages). This questionnaire was administered simultaneously with the household questionnaire. Enumerators administered the questionnaire to a small group (3–5 people) of knowledgeable residents in each village. The profile of respondents included village chief, wife of the village chief, director of the local school, teacher, government officials, assistant land agronomist, religious leaders, merchants, workers in the sector of health, and long-term residents who are well informed. Each of the respondents in the group had lived in the village for several years. Since the goal of this survey was to collect information from authorities responsible for resolving land conflicts at the village level, the interviewers made an effort to include representatives of the following authorities:

1. Village Development Council (CVD)

2. Village Land Conciliation Commission (CCFV)

3. Village Counselors

Enumerators selected two groups for this survey: (1) men and (2) women. There were a total of 3,121 male and female village resident responders (See Exhibit 24). There were up to 15 respondents per village. The groups gathered in a meeting room and were asked to reach a consensus before answering each question. Enumerators noted the disagreements within the group when they were unable to reach a consensus. Groups of men and women were interviewed separately and two responses were collected for each question. There were 1,491 women and 1,630 men.

Exhibit 24: Sample of the Village Survey


Villages 179 178 357

Male respondents 845 785 1,630

Female respondents 786 705 1,491

Total respondents 1,631 1,490 3,121



The village survey used a questionnaire composed of thirteen modules, each focused on a different area of interest (See Exhibit 25).


1. Identification of the village

2. Characteristics of individual respondents

3. Characteristics of village populations

4. Village infrastructure

5. Land use

6. Land ownership arrangements

7. Village resource management

8. Perceptions of land security

9. Perceptions of gender equity in land access

10. Administration of land conflicts at the village level

11. Village organizations

12. Prices of agricultural products

13. Prices of livestock products


Exhibit 26 presents the percent of villages with authorities and institutions responsible for addressing land conflicts. As indicated, 82.9% of the villages reported the presence of a Chef de terre (land ownership chief) and 94.4% reported the presence of a City Council. There is a Village Development Council (Conseil Villageois de Développement, CVD) in practically all villages (98.3%). Only 51.3% of villages had a Village Land Conciliation Commission (Commission de Conciliation Foncière Villageoise, CCFV), with a large difference across treatment (82.1%) and comparison (19.5%) groups. The responses in Exhibit 26 on the number of CCFVs in the treatment and comparison areas are very puzzling and lead us to suspect the data quality of the village survey. Our understanding is that, at the time of the baseline survey, CCFVs were in process of being established and were not operational until after the baseline survey. Therefore, we do not understand how the respondents could have known about the CCFVs. While the 82.1% reported in the treatment areas is surprising, even more surprising is the reported CCFVs in the comparison areas. While MCA explained that some control communes could have set-up their own CCFVs, such a large proportion of CCFVs (19.5%) reported in the control communes remains surprising and puzzeling.


These issues suggest a problem with the village survey which will require a more detailed investigation during the evaluation. In a recent conference call with MCA-BF and MCC, MCA-BF promised to send documentation with detailed information on when CCFVs were established and began operation in each commune. With this documentation, IMPAQ will be able to better assess the validity of the village survey data. 10

Exhibit 26: Percent of Villages with Village Authorities and Institutions11


Chef de terre 86.1% 79.6% 82.9%

City Council 97.2% 91.5% 94.4%

Village Development Council (CVD) 98.3% 98.3% 98.3%

Village Land Conciliation Commission (CCFV) 82.1% 19.5% 51.3%

As indicated in Exhibit 27, a total of 1,980 land conflicts were identified in the treatment and comparison areas during the past 12 months. Specifically, 1,276 land conflicts were reported in villages in the treatment areas and 704 land conflicts were reported in villages in the comparison areas. During the same period, 1,515 were resolved. It is tempting to compare these results with the results reported in Exhibit 18 from the household survey; however, the samples are different and, therefore, the results may not be comparable. As we indicated earlier, the difference in number of land conflicts between the treatment and comparison areas is not a serious data quality issue in measuring the program impacts since the evaluation will control for initial conditions in the two areas.

Exhibit 27: Total Number of Land Conflicts across All Authorities


Total number of identified conflicts 1,276 704 1,980

Total number of resolved conflicts 889 626 1,515

The average number of land disputes presented to various village authorities is depicted in Exhibit 28. As indicated in the exhibit, an average of 2.1 land disputes were presented to the Chef de terre, 1.8 to the City Council, 1.7 to the Village Development Council and 0.7 to the Village Land Conciliation Commission. There low number of land disputes presented to the CCFVs may partly be explained by the issue discussed above. Another issue with the data

10 See conference call notes: MCC_MCA_IMPAQ_MeetingNotes_2014.07.31.docx 11 Only male responses are reported.


presented in Exhibit 28 is that land disputes may have been presented to multiple authorities and, therefore, there is probably some double counting involved in the data. All of these issues will need to be investigated more fully to determine the accuracy of the village data.

Exhibit 28: Average Number of Land Disputes Presented to the Village Authorities


Chef de terre 2.5 1.7 2.1

City Council 2.2 1.3 1.8

Village Development Council (CVD) 2.1 1.3 1.7

Village Land Conciliation Commission (CCFV) 0.8 0.4 0.7

The average number of resolved land disputes at the separate village authorities is presented in Exhibit 29. An average of 2.3 land disputes were resolved by the Chef de terre, 1.6 by the City Council, 2.0 by the Village Development Council and 0.8 by the Village Land Conciliation Commission. A comparison of the results in Exhibit 28 and 29 suggests that, for some village authorities, the responses suggest that more land disputes resolved (Exhibit 28) than were presented (Exhibit 27). For example, Exhibit 29 indicates that CVDs, on average resolved 2.3 land disputes while they were presented 2.1 land disputes, on average (Exhibit 28). This result is puzzling and raises issue on the accuracy of the data.

Exhibit 29: Average Number of Resolved Land Disputes by the Village Authorities


Chef de terre 2.5 2.1 2.3

City Council 1.8 1.4 1.6

Village Development Council (CVD) 2.3 1.7 2.0

Village Land Conciliation Commission (CCFV) 1.0 0.4 0.8


Although we are not aware of specific data collection issues in the field, a review of the data suggests that there are a number of data issues that could affect the quality of the data sets for analysis. Some of the specific issues are discussed below. One of the serious problems identified is the reported number of CCFVs at baseline in treatment and comparison areas. As explained above, although both treatment and


comparison communes might have set-up their own CCFVs at baseline, it is unclear whether they were operational at the time of the baseline survey, yet the data indicates that they were in place. More disturbing is the fact that the data indicates that CCFVs in both treatment and comparison areas were resolving land disputes. If CCFVs were not in operation at the time of the baseline, how could this be? Given these serious data issues, we suspect that the data might not be usable for the impact analysis.


6. COMMUNE SURVEY

The purpose of this survey was to collect information on the characteristics of communes, land conflict administration, and total land surface area. This survey covers all 59 rural communes in both the treatment and comparison areas. The information collected in the questionnaire seeks to complement the information from the household and village questionnaires.

6.1 Number of Interviewed Individuals

The commune-level data was collected from 204 male and female community leaders occupying various community positions, such as municipal counsel and religious chiefs (See Exhibit 30). There were up to five individual respondents per commune and one response was collected for each commune question. There were 33 women among the 204 respondents. Interviewers administered the questionnaire to a group of representatives of the commune and/or commune staff. Informants were required to have worked in the commune for a number of years. The groups gathered in a meeting room and had to reach a consensus before answering each question. Enumerators reported disagreements when the group was unable to reach a consensus.

Exhibit 30: Sample of the Commune Survey


Communes 30 29 59

Male respondents 89 82 171

Female respondents 19 14 33

Total respondents 108 96 204


The commune survey used a questionnaire composed of five modules, each focused on a different area of interest (See Exhibit 31).



1. Identification of the commune

2. Characteristics of individual respondents

3. Activities in the commune in the past 12 months

4. Administration of land conflicts and services at the commune level

5. Total land area


Household Land conflicts were identified in 47 out of 59 communes (See Exhibit 32). Specifically, 26 communes in the treatment areas and 21 communes in the comparison areas had land conflicts. There were a total of 734 land conflicts reported in the last 12 months (431 in treatment communes and 303 in comparison communes). Over the same period, 441 were resolved (227 in treatment and 214 in comparison communes). On average, 16.0 land conflicts were reported per commune and 10.5 were resolved. The responses also indicated that 40 communes had access to land ownership services (services fonciers ruraux, SFR), with large differences across treatment (30) and comparison (10) groups. This last result appears questionable. Although control communes might set-up their own SFRs, the large number of control communes (33%) listing a SFR at baseline is puzzling since SFRs may not have been operational at the time of the baseline. Thus, this remains a potential data quality issue that will be investigated further upon receipt of documentation about detailed implementation dates of SFRs in the various communes.

Exhibit 32: Commune Characteristics


Total number of communes 30 29 59

Communes with identified conflicts 26 21 47

Total number of identified conflicts 431 303 734

Total number of resolved conflicts 227 214 441

Average number of identified conflicts 16.6 15.2 16.0

Average number of resolved conflicts 9.5 11.9 10.5

Communes with SFR 30 10 40



IMPAQ identified some data issues that could affect the overall quality of the data sets for analysis. As described above one data issue is the number of SFRs reported in the treatment and comparison communes. Another issue is the significant number of missing values in important variables. For example, the village land surfaces area was to be collected as part of the commune survey. An examination of the data, however, reveals that the data for many villages are missing. Section E of the questionnaire lists the surface area of all villages in the sample, but many of the entries are missing. Specifically, only 177 villages out of the total 357 villages in all communes are listed and only 40 of these 177 villages have values for the total land surface varaiable. BERD has attributed the missing observations to the lack of official documentation at the commune level to confirm actual surfaces. Interviewers had been instructed to record data after reviewing the available official administrative records and registers. The issues raised above regarding the presence of SFRs and the large number of missing data may present a problem for the usability of the data for the impact analysis. It may be possible to correct the missing data issues since the land area data can still be collected from official records (if they exist). However, such an effort will require additional time and resources to correct. As a result of these issue, it is not clear if the commune survey data is usable for the impact analysis.


7. CONCLUSION

A review of the household and field manger data, suggest that there were some difficulties encountered in the field. However, IMPAQ believes that, in general, the issues were identified and appear to have been corrected by BERD. Furthermore, these issues most likely affected only a small proportion of the sample. As a result, we are confident that the collected household and field manager survey data is adequate for the impact evaluation. On the other hand, the village and commune data may not be of sufficient quality for the impact evaluation. Upon receipt of additional documentation about the various implementation dates of the land institutions, we will be able to investigate these data further. If the village and commune data prove to be “erroneous”, we might not be able to use them for the impact evaluation analysis.


REFERENCES For the preparation of the report, IMPAQ reviewed the following documents: Méthodologie et Plan du Travail. BERD. September 2013. Compte Rendu de Réunion du 02 Octobre sur l’Etat de l’Enquête de Base sur le Foncier Phase II. BERD. October 2013. Rapport Technique sur les Activités de Terrain: Enquête de base sur les ménages et l’agriculture. BERD. January 2014. Rapport de BERD sur la Collecte de Données de Terrain dans le Cadre de la Phase 2 du PSF. BERD. June 2014. Impact Evaluation of MCC Projects in Burkina Faso: Findings from IMPAQ Mission. IMPAQ. February 2012. Preliminary Evaluation Design Report Impact of the Agricultural Development Project and Rural Land Governance Project. IMPAQ. August 2012. Sabo, T. Rapport sur le Contrôle de l’Exécution et des Résultats de l’Enquête Test sur le Foncier – Phase II 2013. September 2013. Memos and emails exchanged between IMPAQ and MCA/MCC over the life of the evaluation project, including:

Comments on Baseline and Follow-up Data Rural Land Governance (RLG) Burkina Faso. IMPAQ. August 16, 2012.

Sampling Phase II Rural Land Goverance Project (RLG) Burkina Faso. IMPAQ. October 17, 2012.

Phase II Rural Land Governance Project (RLG) Comparison Commune Selection Burkina Faso. IMPAQ. November 5, 2012.

Phase II Rural Land Governance Project (RLG) Comparison Commune Selection – Revision Burkina Faso. IMPAQ. December 3, 2012.

Phase II Rural Land Governance Project (RLG) Comparison Commune Selection, Questionnaires and Sampling Burkina Faso. IMPAQ. December 21, 2012.

Phase II RLG Project Village Sampling Burkina Faso. IMPAQ. February 17, 2013.

BERD RLG 2 data files [Stata format].


APPENDIX 1: FINAL STATE OF DATA COLLECTION

TREATED VILLAGES

Village Commune Number of

households to sample

Number of households

sampled

BANZON Banzon 24 24

KOUNSENI Banzon 8 8

YORGO Bere 16 16

BOUNOMTORE Bere 8 8

GOGHIN Bere 8 8

MAZOARA Bere 24 24

KAIBO-SUD V1 Binde 8 8

SONDRE-EST Binde 8 8

LILGOMDE Binde 8 8

TIGRE Binde 8 8

SINIKIERE Binde 8 8

KAIBO-CENTRE Binde 16 16

TOEYOKO Binde 8 8

KOAKIN Binde 16 16

KANKAMOGRE-PEULH Bittou 8 8

ZEKEZE Bittou 8 8

MOGANDE Bittou 16 16

KANYIRE Bittou 8 8

MOGANDE-PEULH Bittou 8 8

LOABA Bittou 16 16

GNAGDIN Bittou 8 8

KANKAMOGRE Bittou 8 8

FOTTIGUE Bittou 8 8

SAWENGA Bittou 16 16

BIRON-MARKA Bourasso 16 16

KAMIANKORO Bourasso 8 8

LEKUY Bourasso 8 8

KASSOLO-TIABONA Cassou 8 8

NEVRY Cassou 8 8

CASSOU Cassou 16 16

OUPON Cassou 8 8

GUILLAN Cassou 8 8

NIESSAN Cassou 8 8

BAZAWARA Cassou 8 8





sampled

KADAPRA Cassou 8 8

OUAYOU Cassou 8 8

KYA Didyr 8 8

POUNI-NORD Didyr 8 8

IMOUGOU Didyr 8 8

LADIANA Didyr 8 8

DIDYR Didyr 16 16

MOUZOUMOU Didyr 16 16

DJIGOUE Djigoue 8 8

BAWE-DARA Djigoue 24 24

GONGOMBOULO Djigoue 8 8

DOUNA Douna 16 16

MONSARA Douna 8 8

TERI-SAMO Gassan 8 8

DJIN Gassan 8 8

SONI Gassan 8 8

DJIMBARA Gassan 8 8

GASSAN Gassan 16 16

WAROU Gassan 8 8

MOARA-GRAND Gassan 8 8

LERY Gassan 8 8

MARA-GRAND Kassoum 8 8

BAN Kassoum 8 8

TOUNGOUROU Kassoum 8 8

BOURGOU Kassoum 8 8

DOUBAN Kassoum 8 8

PINI Kassoum 8 8

SAKOINSE Kokologho 8 8

PITMOAGA Kokologho 8 8

DOURE Kokologho 8 8

GOULOURE Kokologho 8 8

KOKOLOGHO Kokologho 48 48

TANVI Koubri 8 8

TANSABLOGO Koubri 8 8

MOINCE Koubri 8 8

PEELE Koubri 16 16

KOUBRI Koubri 32 32

PIKIEKO Koubri 8 8





sampled

NAPAGTING-GOUNGHIN Koubri 8 8

DIDRI Koubri 8 8

SABLOGO Lalgaye 8 8

DIBLI Lalgaye 24 24

GOURAN Lanfiera 8 8

KOUMBARA Lanfiera 16 16

GUIEDOUGOU Lanfiera 16 16

MOUSSODOUGOU Moussoudogou 16 16

MONDON Moussoudogou 8 8

TIMPERBA Niangaloko 16 16

DANGOUINDOUGOU Niangaloko 8 8

KIMINI Niangaloko 16 16

NOFESSO Niangaloko 8 8

KARABOROSSO Niangaloko 8 8

OUANGOLODOUGOU Niangaloko 8 8

LAHIRASSO Padema 16 16

KOLEDOUGOU Padema 16 16

KIMINI Padema 8 8

DJIGOUEMA Padema 16 16

BANWALI Padema 8 8

SOMA Padema 8 8

BANKOUMA Padema 8 8

ZONGOMA Padema 16 16

LOAGA Poa 16 16

YARGUIN Poa 8 8

POA Poa 24 24

MENTAO Pobe-Mengao 8 8

CISSE Pobe-Mengao 8 8

GASKINDE Pobe-Mengao 8 8

BOUI-BOUI Pobe-Mengao 24 24

POBE-MENGAO Pobe-Mengao 8 8

ROLLO Rollo 24 24

POGORO Rollo 8 8

GONDEKOUBE Rollo 8 8

POGORO-FOULBE Rollo 8 8

KANGARE Rollo 8 8

BOUDTENGA PEULH Saaba 8 8

TANSOBENTINGA Saaba 16 16





sampled

BAROGO Saaba 8 8

NIOKO I Saaba 48 48

KOALA Saaba 8 8

BADNOGO 2 Saaba 8 8

SAABA Saaba 40 40

BOUDTENGA Saaba 8 8

SIKORLA-DIERIKANDOUGOU Samorougan 8 8

KORKORLA Samorougan 8 8

SAMOROGOUAN Samorougan 24 24

SANA Samorougan 8 8

ZOUMAHIRI Samorougan 8 8

KONGOLIKORO Samorougan 8 8

N'GANA Samorougan 16 16

SANTIO Sapouy 8 8

BORO Sapouy 8 8

NABILPAGA-YORGA Sapouy 16 16

TIABIEN Sapouy 8 8

BAOUIGA Sapouy 8 8

LADIGA Sapouy 8 8

NEKROU Sapouy 8 8

LATIAN Sapouy 8 8

POUN Sapouy 8 8

KOUTERA Sapouy 8 8

BATE Sideradougou 8 8

GOUIN-GOUIN Sideradougou 8 8

KOUENDI Sideradougou 8 8

DEGUE-DEGUE Sideradougou 16 16

GOUANDOUGOU Sideradougou 24 24

KAPONGOUAN Sideradougou 8 8

KOUERE Sideradougou 16 16

SAMPOBIN Sideradougou 16 16

DANDOUGOU Sideradougou 8 8

DJANGA Sideradougou 8 8

DOUTIE Sideradougou 8 8

DEREGOUE I Sideradougou 8 8

BADE Sideradougou 16 16

SIDERADOUGOU Sideradougou 8 8

KELEGUERIMA Tangaye 8 8





sampled

BOUNDOUKAMBA Tangaye 8 8

DOUMA Tangaye 16 16

LOUBRE Tangaye 8 8

MERA Tangaye 8 8

NAMSIGUIA Tangaye 8 8

RASSANDOGO Tangaye 8 8

TOPTIAGOU Tansarga 8 8

TANSARGA Tansarga 24 24

BODIAGA Tansarga 8 8

KOBDARI Tansarga 8 8

DIAFOUANOU Tansarga 8 8

BOBOMONDI Tansarga 8 8

POUN Tenado 8 8

TENADO Tenado 8 8

TIEBO Tenado 8 8

TIOGO Tenado 8 8

BATONDO Tenado 8 8

TIALGO Tenado 16 16

KOUKOULDI Tenado 24 24

KABORO Tenado 8 8

DOUDOU Tenado 8 8

TOUSSIANA Toussiana 8 8

NIANABA Toussiana 24 24

WEMPEA II Toussiana 8 8

GASDONKA Zimtenga 8 8

KALAGRE-MOSSI Zimtenga 8 8

KALAGRE-RIMAIBE Zimtenga 8 8

BANGRIN Zimtenga 8 8

MINIMA Zimtenga 8 8

BATANGA Zimtenga 8 8

TOTAL NUMBER OF INTERVIEWS 2008 2008


CONTROL VILLAGES

Village Commune

Number of

households to

sample

Number of

households

sampled

BAKATA Bakata 16 16

BASNERE Bakata 8 8

BIYENE Bakata 8 8

BOULE-GALA Bakata 8 8

KINKIRSGOGO Bakata 8 8

TAYALO Bakata 8 8

BARABOULE Baraboule 8 8

DANKANAO Baraboule 16 16

DOTOKA Baraboule 8 8

HOCOULOUROU Baraboule 16 16

OUDOUGA Baraboule 8 8

PAHOUNDE Baraboule 8 8

PETEGOLI Baraboule 8 8

BAGLENGA CominYanga 8 8

COMIN-YANGA CominYanga 16 16

GAONGHIN CominYanga 8 8

KIOUGOU-DOURE CominYanga 8 8

KIOUGOU-NAMOUNOU CominYanga 8 8

KOLANGA CominYanga 8 8

VOHOGDIN CominYanga 16 16

ZONGHIN CominYanga 16 16

DAKORO Dakoro 8 8

LEMAGARA Dakoro 8 8

MOADOUGOU Dakoro 8 8

DOKUY Dokuy 8 8

ILABEKOLON Dokuy 8 8

KANADOUGOU Dokuy 16 16

KOLONKOURA Dokuy 8 8

MAKUY Dokuy 8 8

SOKOURA Dokuy 8 8

SOUMAKORO Dokuy 8 8

BOEBANGO Gamboussougou 16 16

DINDEOGO Gamboussougou 8 8

DIRZE Gamboussougou 8 8

FOUNGOU Gamboussougou 8 8


Village Commune

Number of

households to

sample

Number of

households

sampled

GONBOUSSOU Gamboussougou 32 32

MEDIGA Gamboussougou 16 16

MOUNIBAOGO Gamboussougou 8 8

TINGUEMNORE Gamboussougou 8 8

BAHOUAN Gbomblora 8 8

DALAMPOUR Gbomblora 8 8

GOGOMBIRO Gbomblora 8 8

HOUIEO Gbomblora 8 8

SEWERA Gbomblora 8 8

GOUROU Godyr 8 8

KONEGA Godyr 8 8

NAPOUAN Godyr 8 8

SIENNE Godyr 8 8

DOUGOU Gogo 8 8

GOGO Gogo 8 8

IPALA Gogo 8 8

KOPELIN Gogo 16 16

MANGA-EST V3 Gogo 8 8

MOUZI Gogo 8 8

PISSI Gogo 8 8

SAFOULA Gogo 8 8

ZIRBARE Gogo 8 8

BETARE Guiaro 8 8

BOUYA Guiaro 8 8

KOUMBILI Guiaro 8 8

NISSARE Guiaro 8 8

PORE Guiaro 8 8

KINDI Kindi 8 8

KONE Kindi 16 16

NASSOULOU Kindi 16 16

ZERKOUM Kindi 16 16

BANGMA Komsilga 8 8

DAWANEGOMDE Komsilga 16 16

GOOGHO Komsilga 8 8

PAMNONGHIN Komsilga 8 8

SABTOANA Komsilga 8 8

TANG-SEGA Komsilga 8 8


Village Commune

Number of

households to

sample

Number of

households

sampled

TINGANDOGO Komsilga 96 96

KAMBA Kougny 16 16

KOUGNY Kougny 8 8

NIARE Kougny 8 8

FARA Kourouma 8 8

GUIGUIMA Kourouma 8 8

KOKORO Kourouma 8 8

KOUROUMA Kourouma 32 32

SADINA Kourouma 16 16

SOUGOUMA Kourouma 8 8

BOUNOUBA Mangodara 8 8

DANDOUGOU Mangodara 8 8

DIARADOUGOU Mangodara 8 8

FARAKOROSSO Mangodara 16 16

GONTIEDOUGOU Mangodara 8 8

LOGOGNIEGUE Mangodara 8 8

MADIASSO Mangodara 8 8

MANGODARA Mangodara 8 8

NOUMOUTIEDOUGOU Mangodara 24 24

TORANDOUGOU Mangodara 8 8

TOUROUKORO Mangodara 8 8

BOULPON Nanoro 16 16

DACISSE Nanoro 8 8

GOUROUMBILA Nanoro 8 8

NANORO Nanoro 16 16

SEGUEDIN Nanoro 8 8

SOALA Nanoro 8 8

BILIGA-MOSSI Nassere 8 8

NASSERE Nassere 16 16

GASKAYE Pabre 8 8

KOANKIN Pabre 8 8

PABRE Pabre 24 24

SABTENGA Pabre 8 8

SAG-NIONIOGO Pabre 8 8

DOGOSSESSO Peni 8 8

FINLANDE Peni 8 8

KODARA Peni 8 8


Village Commune

Number of

households to

sample

Number of

households

sampled

KOUMANDARA Peni 8 8

LANFIERA Peni 8 8

MARABAGASSO Peni 8 8

ME Peni 8 8

NONKONDOUGOU Peni 8 8

PENI Peni 8 8

BAGANAPOUN Pouni 16 16

BANDEO-NAPONE Pouni 8 8

ELINGA Pouni 8 8

LILBOURE Pouni 8 8

TIEKOUYOU Pouni 8 8

TIYELLE Pouni 8 8

VALIOU Pouni 8 8

VILLY Pouni 8 8

SAMOGOHIRI Samoghiri 16 16

BALLA Satiri 8 8

BOSSORA Satiri 16 16

KADOMBA Satiri 8 8

KOROMA Satiri 16 16

MOLOKADOUN Satiri 8 8

SATIRI Satiri 8 8

SISSA Satiri 8 8

WEROU Satiri 8 8

GORDAGA Sindo 8 8

NIAMANA Sindo 16 16

012 SINDO Sindo 16 16

FOMOFESSO SOUBAKANIEDOUGOU 8 8

GOUERA SOUBAKANIEDOUGOU 8 8

LETIEFESSE SOUBAKANIEDOUGOU 8 8

SOUBAKANIEDOUGOU SOUBAKANIEDOUGOU 32 32

ZIEDOUGOU SOUBAKANIEDOUGOU 8 8

BAAKA Tambaga 8 8

BAYENTORI Tambaga 8 8

DIADORI Tambaga 8 8

KONLI I Tambaga 16 16

MOMBA-PEULH Tambaga 8 8

PENTINGA-GOURMANTCHE Tambaga 8 8


Village Commune

Number of

households to

sample

Number of

households

sampled

SANSANGA Tambaga 16 16

BANI Thiou 8 8

GARDERE Thiou 8 8

GONNA Thiou 8 8

SAMBABOULI Thiou 16 16

SANGA Thiou 8 8

SIM Thiou 16 16

TALLE Thiou 8 8

THIOU Thiou 32 32

GASONGO Tikare 8 8

MANEGTABA-FOULBE Tikare 8 8

RITIMYINGA Tikare 8 8

SOUKOUNDOUGOU Tikare 8 8

TANHOKA Tikare 16 16

TIKARE Tikare 8 8

TOUGA Tikare 8 8

VATO-BOULDE Tikare 8 8

BIBA Yaba 16 16

BOUNOU Yaba 16 16

LOGUIN Yaba 8 8

TOSSON Yaba 8 8

YABA Yaba 16 16

BARGANSE-PEULH Zabre 8 8

BEKA Zabre 8 8

BISSAYA Zabre 8 8

GUIRMOGO Zabre 8 8

SAMPEMA Zabre 24 24

TOUBISSA Zabre 8 8

WANGALA Zabre 16 16

YOROKO Zabre 8 8

ZIHOUN Zabre 8 8

ZOURMA Zabre 24 24

ZABRE Zabre 56 56

TOTAL NUMBER OF INTERVIEWS 2008 2008


APPENDIX 2: BASIC DESCRIPTIVE STATISTICS

The purpose of this section is to present descriptive statistics of some key variables/sections of the village, household, and field manager questionnaires.

1. Village Questionnaire

The following tables describe male and female responses to questions related to village land access and resources. The management and access to common resources is an important aspect of land reform and we would expect that men and women respond similarly to questions that are more “objective” in nature (like whether the village has a given type of land or not). Other questions might require more extensive type of knowledge, or the issue at hand might be perceived differently by the two groups. Some examples are described in more detail in the tables below. Table 1 describes the percentage of respondents who responded “yes” to the following questions: (i) whether the village has arable land that is not allocated; (ii) whether the village can determine rule of access to this land and (iii) whether this land has been source of conflict with neighboring villages.

Table 1 : Existence and Access to Arable Land that is not Allocated, by Region and Gender

Does the village has arable land that is

not allocated? (1)

Is the village able to determine rules of

access to arable unallocated land?

(2)

Are there conflicts with other villages on the arable land that is

not allocated? (3)

Region Sex Sex Sex

Male Female Male Female Male Female

BOUCLE DU MOUHOUN 6% 6% 100% 50% 50% 0%

CASCADES 30% 33% 85% 85% 23% 31%

CENTRE 71% 64% 60% 61% 15% 6%

CENTRE EST 39% 37% 42% 45% 17% 9%

CENTRE NORD 43% 38% 78% 100% 22% 25%

CENTRE 30% 33% 57% 43% 10% 9%

CENTRE SUD 21% 21% 57% 71% 57% 29%

EST 15% 8% 100% 100% 0% 0%

HAUTS BASSINS 48% 49% 95% 95% 36% 39%

NORD 40% 60% 50% 67% 0% 22%

SAHEL 17% 17% 100% 100% 50% 50%

SUD OUEST 0% 13% 0% 0% Source: IMPAQ computations based on Village data, Section E3 (Questions E3.1, E3.3, and E3.4)

The data in the table suggest that male and female respondents generally agree about the fact that the village has arable land that is not allocated (column 1), although there are regional


differences: for example more males than females in the EST region (15% vs. 8%) think that the village has non-allocated arable land, while the reverse is true for the NORD region (40% vs. 60%). These large differences might indicate a different level of knowledge between female and male respondents about the availability of arable land in those regions. More differences emerge in the responses between men and females for the other two issues presented: (i) i.e. whether the village can determine rules of access to non-allocated arable land and (ii) whether there are conflicts with neighboring villages about this land. Differences in response rates on these matters might indicate a different level of knowledge and or perceptions about issues like access to land and land conflicts. The same questions are asked regarding other common resources of the village: forestry, pastures and rivers. The results are presented in Tables 2-4.

Table 2 Existence and Access to Forests by region and gender

Does the village has forests?

(1)

Is the village able to determine rules of access to forestry

resources? (2)

Are there conflicts with other villages about the forest?

(3)

Region Sex Sex Sex



CASCADES 53% 47% 91% 84% 17% 21%

CENTRE 43% 43% 45% 45% 18% 9%

CENTRE EST 52% 57% 31% 24% 19% 6%

CENTRE NORD 14% 24% 100% 60% 0% 20%

CENTRE 54% 54% 78% 68% 22% 22%

CENTRE SUD 41% 47% 79% 88% 7% 0%

EST 15% 15% 100% 100% 50% 50%

HAUTS BASSINS 59% 60% 100% 96% 7% 4%

NORD 27% 33% 75% 80% 0% 20%

SAHEL 17% 17% 50% 50% 0% 0%

SUD OUEST 13% 25% 0% 0% 0% 0% Source: IMPAQ computations based on Village data, Section E3 (Questions E3.1, E3.3, and E3.4)

The results of Table 2 indicate that men and women generally agree on the fact that the village has some forests, however when it comes to express knowledge of whether the village can determine rule of access to the use of the forest and whether the forestry resources are subject of controversies with neighboring villages, opinion tend to differ more sharply between men and women. For example, all men in the CENTRE-NORD regions believe that village can determine rules of access to the forest, compared to only 60% of women respondents, while 20% of women and none of the men.


Table 3 Existence and Access to pastures by region and gender

Does the village have pastures?

(1)

Is the village able to determine rules of access to pastures?

(2)

Are there conflicts with other villages on

the pastures? (3)

Region Sex Sex Sex



CASCADES 30% 23% 77% 44% 23% 0%

CENTRE 32% 29% 78% 75% 22% 25%

CENTRE EST 81% 83% 24% 20% 8% 8%

CENTRE NORD 62% 76% 54% 50% 23% 13%

CENTRE 57% 59% 62% 63% 23% 15%

CENTRE SUD 56% 56% 58% 68% 32% 17%

EST 38% 54% 40% 14% 40% 0%

HAUTS BASSINS 83% 85% 79% 84% 16% 8%

NORD 67% 60% 70% 67% 10% 22%

SAHEL 75% 75% 0% 0% 11% 11%

SUD OUEST 50% 50% 25% 25% 0% 0% Source: IMPAQ computations based on Village data, Section E3 (Questions E3.1, E3.3, and E3.4)

The results of Table 3 indicate that men and women generally agree on the fact that the village has some land for pastures, however when it comes to express knowledge of whether the village can determine rule of access to the use of this land and whether the pastures are subject of controversies with neighboring villages, opinion tend to differ more sharply between men and women. Similar results hold for questions about presence of rivers in the village.


Table 4 Existence and Access to rivers by region and gender

Does the village have water courses?

(1)

Is the village able to determine rules of

access to water courses?

(2)

Are there conflicts with other villages

about the water courses?

(3)

Region Sex Sex Sex



CASCADES 60% 57% 85% 83% 12% 17%

CENTRE 46% 46% 54% 54% 15% 8%

CENTRE EST 58% 60% 33% 22% 6% 6%

CENTRE NORD 38% 38% 57% 38% 14% 13%

CENTRE 69% 69% 67% 67% 6% 4%

CENTRE SUD 65% 59% 68% 70% 14% 11%

EST 46% 31% 17% 25% 0% 0%

HAUTS BASSINS 98% 98% 80% 77% 16% 13%

NORD 20% 13% 67% 0% 0% 0%

SAHEL 33% 33% 0% 0% 0% 0%

SUD OUEST 25% 25% 50% 50% 0% 0%

Source: IMPAQ computations based on Village data, Section E3 (Questions E3.1, E3.3, and E3.4)

The village questionnaire also asks about price data for various crops, the price at harvest and the minimum and maximum prices recorded at the village level. Prices are reported per crop and per unit of measure used to report the crop, which vary by village. Table 5 shows the average prices across villages reporting any given crop and using the same unit of measure (for the 3 most important crops produced as described in Exhibit 20). An inspection of the price data shows that there are approximately 19 villages (out of 357) where only one group (men and women) report prices. In addition for many records (approximately 40%) the price at harvest is smaller (or lager) than the minimum (maximum) price recorded at village level. The table shows that men and women tend to report crops in the same units, but they tend to report different prices, on average.


Table 5: Prices by Crop and Unit, Male and Female Respondents

Male

Female

Crop Unit of Measure Price at harvest

Min price in village

Max price in village

Crop Unit of Measure

Price at harvest

Min price in village

Max price in village

Sorgho Boite de tomate 191 217 309 Sorgho Boite de tomate 199 216 312

Sorgho Kilogramme 100 100 164 Sorgho Kilogramme 100 96 158

Sorgho Plat Yorouba 335 363 510 Sorgho Plat Yorouba 348 374 538

Sorgho Sac de 100 kilogrammes 12727 13500 15152 Sorgho Sac de 100 kilogrammes 13160 14320 16360

Sorgho Sac de 50 kilogrammes 6217 10950 12900 Sorgho Sac de 50 kilogrammes 35035 0 750

Sorgho Tine 1904 1976 2911 Sorgho Tine 1825 1776 2841

Millet/petit mil Boite de tomate 249 274 409 Millet/petit mil Boite de tomate 254 267 409

Millet/petit mil Kilogramme 118 118 189 Millet/petit mil Kilogramme 125 125 197

Millet/petit mil Plat Yorouba 445 489 657 Millet/petit mil Plat Yorouba 422 443 674

Millet/petit mil Sac de 100 kilogrammes 13295 13920 18446 Millet/petit mil Sac de 100 kilogrammes 13976 18238 18286

Millet/petit mil Sac de 50 kilogrammes 6675 6675 8850 Millet/petit mil Sac de 50 kilogrammes 350 350 750

Millet/petit mil Tine 2307 2607 3964 Millet/petit mil Tine 2361 2637 4171

Mais Boite de tomate 156 186 289 Mais Boite de tomate 160 180 285

Mais Kilogramme 107 107 171 Mais Kilogramme 108 108 175

Mais Plat Yorouba 324 348 507 Mais Plat Yorouba 339 365 525

Mais Sac de 100 kilogrammes 11167 12348 15167 Mais Sac de 100 kilogrammes 10266 11640 14146

Mais Sac de 50 kilogrammes 12750 10875 15500 Mais Sac de 50 kilogrammes 13500 11250 16000

Mais Tine 1858 1772 2759 Mais Tine 1533 1427 2452

Mais Unité 67 78 139 Mais Unité 63 69 156


2. Household Questionnaire

2.1 Household Living Conditions

The next set of tables present some variables describing some household basic characteristics and household living conditions, including how the residence where the household head lives was acquired, nature of the walls, access to electricity, type of latrines and main source of water. For example, Table 6 shows that the majority of households are headed by a male, although some regional differences exist: while only 3% of households have female household heads, the percentage is almost 11% in the CENTRE region. Regarding household living conditions, in almost 80% of cases (across all regions) the household owns the house (either bought or constructed the building, Table 7). In addition, the majority of building (75.5%) are constructed using bricks (either clay or dirt), although some regional differences emerge (Table 8).

Table 6: Distribution of household heads by region and gender

Region N %

Male Female Total

BOUCLE DU MOUHOUN N 336 17 353

% 95.18 4.82 100

CASCADES N 458 15 473

% 96.83 3.17 100

CENTRE N 399 48 447

% 89.26 10.74 100

CENTRE EST N 340 60 400 % 85 15 100

CENTRE NORD N 183 17 200

% 91.5 8.5 100

CENTRE N 672 63 735

% 91.43 8.57 100

CENTRE SUD N 333 27 360

% 92.5 7.5 100

EST N 127 9 136

% 93.38 6.62 100

HAUTS BASSINS N 519 18 537

% 96.65 3.35 100

NORD N 161 6 167 % 96.41 3.59 100

SAHEL N 125 4 129

% 96.9 3.1 100

SUD OUEST N 73 7 80

% 91.25 8.75 100

Total N 3,726 291 4,017

% 92.76 7.24 100


Table 7: Occupation status of the residence where the household head lives

Region Numb.

%

Owner (bought the

property)

Owner (build the property)

Inherited Other

ownership Rented

Occupied without paying

rent Other Total

BOUCLE DU MOUHOUN N 4 251 76 8 3 9 1 352

% 1.14 71.31 21.59 2.27 0.85 2.56 0.28 100

CASCADES N 15 369 59 9 11 9 0 472

% 3.18 78.18 12.5 1.91 2.33 1.91 0 100

CENTRE N 14 354 40 3 11 25 0 447

% 3.13 79.19 8.95 0.67 2.46 5.59 0 100

CENTRE EST N 5 320 34 4 11 26 0 400

% 1.25 80 8.5 1 2.75 6.5 0 100

CENTRE NORD N 0 161 35 0 2 2 0 200

% 0 80.5 17.5 0 1 1 0 100

CENTRE N 10 532 143 8 10 29 0 732

% 1.37 72.68 19.54 1.09 1.37 3.96 0 100

CENTRE SUD N 6 299 41 3 3 7 1 360

% 1.67 83.06 11.39 0.83 0.83 1.94 0.28 100

EST N 3 110 18 0 1 3 0 135

% 2.22 81.48 13.33 0 0.74 2.22 0 100

HAUTS BASSINS N 7 416 69 16 8 20 0 536

% 1.31 77.61 12.87 2.99 1.49 3.73 0 100

NORD N 1 133 30 0 0 3 0 167

% 0.6 79.64 17.96 0 0 1.8 0 100

SAHEL N 0 112 16 0 0 1 0 129

% 0 86.82 12.4 0 0 0.78 0 100

SUD OUEST N 0 62 15 0 2 1 0 80

% 0 77.5 18.75 0 2.5 1.25 0 100

Total N 65 3,119 576 51 62 135 2 4,010

% 1.62 77.78 14.36 1.27 1.55 3.37 0.05 100 Source: IMPAQ computations based on household data, Section C (Questions C02A)


Table 8: Nature of the of residence where the household head walls by region

Region N %

Concrete Stone Bricks (clay)

Banco Improved

Banco (mud brick)

Clay brick Straw Other Total

BOUCLE DU MOUHOUN N 6 7 1 27 269 41 2 0 353

% 1.7 1.98 0.28 7.65 76.2 11.61 0.57 0 100

CASCADES N 47 11 23 51 321 13 4 0 470

% 10 2.34 4.89 10.85 68.3 2.77 0.85 0 100

CENTRE N 43 0 6 68 292 30 5 3 447

% 9.62 0 1.34 15.21 65.32 6.71 1.12 0.67 100

CENTRE EST N 56 0 11 52 232 33 12 2 398

% 14.07 0 2.76 13.07 58.29 8.29 3.02 0.5 100

CENTRE NORD N 0 0 4 8 172 13 3 0 200

% 0 0 2 4 86 6.5 1.5 0 100

CENTRE N 24 2 4 114 525 56 5 0 730

% 3.29 0.27 0.55 15.62 71.92 7.67 0.68 0 100

CENTRE SUD N 43 0 6 91 155 44 18 3 360

% 11.94 0 1.67 25.28 43.06 12.22 5 0.83 100

EST N 4 0 1 6 90 29 5 1 136

% 2.94 0 0.74 4.41 66.18 21.32 3.68 0.74 100

HAUTS BASSINS N 25 31 26 32 404 16 0 2 536

% 4.66 5.78 4.85 5.97 75.37 2.99 0 0.37 100

NORD N 4 1 0 24 132 0 5 0 166

% 2.41 0.6 0 14.46 79.52 0 3.01 0 100

SAHEL N 1 0 0 2 77 9 40 0 129

% 0.78 0 0 1.55 59.69 6.98 31.01 0 100

SUD OUEST N 1 0 1 6 71 1 0 0 80

% 1.25 0 1.25 7.5 88.75 1.25 0 0 100

Total N 254 52 83 481 2,740 285 99 11 4,005

% 6.34 1.3 2.07 12.01 68.41 7.12 2.47 0.27 100

Source: IMPAQ computations based on household data, Section C (Questions C08)


Regarding household mode of lighting, in almost 65% of cases (across all regions) the household main mode of lighting is by flashlight/batteries (Table 9). In addition, the majority of buildings (60%) do not have any type of latrine in the household (Table 10). Finally, Table 11 shows that most households do not have a source of drinking water in the house but 44% of household have “Forage” as main source of drinking water.


Table 9 Mode of lighting in the house by region

Region N %

Oil/petroleum

Electricity Solar

energy Electrogen

group Candles

Lamp/Batteries

Flashlight/batteries

Wood Other Total

BOUCLE DU MOUHOUN

N 5 21 26 0 0 85 210 2 0 349

% 1.43 6.02 7.45 0 0 24.36 60.17 0.57 0 100

CASCADES N 1 8 67 0 4 153 235 1 1 470

% 0.21 1.7 14.26 0 0.85 32.55 50 0.21 0.21 100

CENTRE N 8 23 12 1 0 139 261 0 3 447

% 1.79 5.15 2.68 0.22 0 31.1 58.39 0 0.67 100

CENTRE EST N 5 14 10 2 1 23 334 2 0 391

% 1.28 3.58 2.56 0.51 0.26 5.88 85.42 0.51 0 100

CENTRE NORD N 0 1 7 1 1 39 147 4 0 200

% 0 0.5 3.5 0.5 0.5 19.5 73.5 2 0 100

CENTRE N 58 13 38 0 0 110 505 3 0 727

% 7.98 1.79 5.23 0 0 15.13 69.46 0.41 0 100

CENTRE SUD N 1 2 11 0 0 73 264 1 5 357

% 0.28 0.56 3.08 0 0 20.45 73.95 0.28 1.4 100

EST N 1 5 1 0 0 4 124 0 0 135

% 0.74 3.7 0.74 0 0 2.96 91.85 0 0 100

HAUTS BASSINS N 21 21 99 0 1 193 191 2 2 530

% 3.96 3.96 18.68 0 0.19 36.42 36.04 0.38 0.38 100

NORD N 2 0 4 0 0 21 137 1 1 166

% 1.2 0 2.41 0 0 12.65 82.53 0.6 0.6 100

SAHEL N 0 0 5 0 0 10 113 0 1 129

% 0 0 3.88 0 0 7.75 87.6 0 0.78 100

SUD OUEST N 0 0 7 0 0 6 66 0 1 80

% 0 0 8.75 0 0 7.5 82.5 0 1.25 100

Total N 102 108 287 4 7 856 2,587 16 14 3,981

% 2.56 2.71 7.21 0.1 0.18 21.5 64.98 0.4 0.35 100



Table 10 Type of latrines by region

Region N %

Septic pit

Traditional latrine

Latrine/Pit

Latrine or

Manuel Flush Toilet

Latrine/ Sanitation Platform

Ecological Sanitation

Latrines

In the nature

(no latrine)

Total

BOUCLE DU MOUHOUN

N 9 159 1 1 1 0 182 353 % 2.55 45.04 0.28 0.28 0.28 0 51.56 100

CASCADES N 6 211 0 0 14 8 232 471 % 1.27 44.8 0 0 2.97 1.7 49.26 100

CENTRE N 14 230 9 8 14 2 170 447

% 3.13 51.45 2.01 1.79 3.13 0.45 38.03 100

CENTRE EST N 3 62 2 9 0 0 320 396

% 0.76 15.66 0.51 2.27 0 0 80.81 100

CENTRE NORD N 11 91 22 0 1 1 74 200

% 5.5 45.5 11 0 0.5 0.5 37 100

CENTRE N 10 140 0 0 11 37 536 734

% 1.36 19.07 0 0 1.5 5.04 73.02 100

CENTRE SUD N 1 56 3 0 1 0 299 360 % 0.28 15.56 0.83 0 0.28 0 83.06 100

EST N 1 8 2 0 1 0 124 136 % 0.74 5.88 1.47 0 0.74 0 91.18 100

HAUTS BASSINS

N 3 306 2 4 12 9 200 536

% 0.56 57.09 0.37 0.75 2.24 1.68 37.31 100

NORD N 10 41 1 0 2 0 113 167

% 5.99 24.55 0.6 0 1.2 0 67.66 100

SAHEL N 0 30 0 0 0 0 99 129

% 0 23.26 0 0 0 0 76.74 100

SUD OUEST N 1 11 0 1 8 0 59 80

% 1.25 13.75 0 1.25 10 0 73.75 100

Total N

69 1,345 42 23 65 57 2,408 4,009

% 1.72 33.55 1.05 0.57 1.62 1.42 60.06 100 Source: IMPAQ computations based on household data, Section C (Questions C10)


Table 11 Main source of drinking water by region

Région

N %

River/dam Dug well Uncovered

Wells Covered

Wells Forage Public

fountain Indoor

private tap Outdoor

shared tap Other Total

BOUCLE DU MOUHOUN N

1 117 89 14 97 27 0 7 1 353 %

0.28 33.14 25.21 3.97 27.48 7.65 0 1.98 0.28 100

CASCADES N

48 67 18 6 214 108 3 7 0 471 %

10.19 14.23 3.82 1.27 45.44 22.93 0.64 1.49 0 100

CENTRE N

8 16 14 6 186 129 19 69 0 447 %

1.79 3.58 3.13 1.34 41.61 28.86 4.25 15.44 0 100

CENTRE EST N

22 51 67 20 167 61 3 7 2 400

% 5.5 12.75 16.75 5 41.75 15.25 0.75 1.75 0.5 100

CENTRE NORD N

7 24 31 3 126 9 0 0 0 200

% 3.5 12 15.5 1.5 63 4.5 0 0 0 100

CENTRE N

10 118 117 44 347 85 6 7 0 734 %

1.36 16.08 15.94 5.99 47.28 11.58 0.82 0.95 0 100

CENTRE SUD N

13 12 34 2 267 30 1 1 0 360 %

3.61 3.33 9.44 0.56 74.17 8.33 0.28 0.28 0 100

EST N

5 25 17 0 88 0 1 0 0 136 %

3.68 18.38 12.5 0 64.71 0 0.74 0 0 100

HAUTS BASSINS N

14 239 108 38 93 39 1 4 0 536 %

2.61 44.59 20.15 7.09 17.35 7.28 0.19 0.75 0 100

NORD N

3 54 16 5 76 13 0 0 0 167

% 1.8 32.34 9.58 2.99 45.51 7.78 0 0 0 100

SAHEL N

20 8 16 0 81 3 0 0 0 128

% 15.63 6.25 12.5 0 63.28 2.34 0 0 0 100

SUD OUEST N

6 1 7 2 33 30 1 0 0 80 %

7.5 1.25 8.75 2.5 41.25 37.5 1.25 0 0 100

Total N

157 732 534 140 1,775 534 35 102 3 4,012 %

3.91 18.25 13.31 3.49 44.24 13.31 0.87 2.54 0.07 100



2.2 Land Transfers

The next set of tables show some descriptive statistics from Section F of household questionnaire. This section of the household questionnaire inquires whether the household had some land in the past in 3 years that is no longer in possession of the household and which has been transferred to someone else outside the household. This includes also land which was lost because of land conflicts. Table 12 indicates that 95% of household (across all regions) did not transfer any land in the past 3 years. Among those who transferred/lost the land, at most 2 fields had been transferred, and in most cases these fields had been used for agricultural purposes (Table 13). In addition, as shown in Table 14, a substantial portion of fields used for agricultural purposes (almost 30%) was lost because of land conflicts.


Table 12 Land transfer in the last 3 years

Whether the HH has transferred land in the past 3 years

Region N %

Yes No Total

BOUCLE DU MOUHOUN N 8 345 353

% 2.27 97.73 100

CASCADES N 15 457 472 % 3.18 96.82 100

CENTRE N 29 418 447

% 6.49 93.51 100

CENTRE EST N 17 381 398

% 4.27 95.73 100


% 3.5 96.5 100

CENTRE N 30 703 733

% 4.09 95.91 100

CENTRE SUD N 25 335 360

% 6.94 93.06 100

EST N 1 135 136 % 0.74 99.26 100


% 8.77 91.23 100

NORD N 3 164 167

% 1.8 98.2 100

SAHEL N 8 121 129

% 6.2 93.8 100

SUD OUEST N 1 79 80

% 1.25 98.75 100

Total N 191 3,820 4,011

% 4.76 95.24 100 Source: IMPAQ computations based on household data, Section F (Questions F00)


Table 13 Average number of fields transferred, total and by need, by region

Region

Number of fields

transferred in the last 3

years

Number of fields transferred used for :

Agriculture Housing Livestock Other

BOUCLE DU MOUHOUN 1 1 0 0 0

CASCADES 1 1 0 0 0

CENTRE 1 1 0 0 0

CENTRE EST 2 2 0 0 0

CENTRE NORD 1 1 0 0 0

CENTRE 2 2 0 0 0

CENTRE SUD 2 1 1 0 1

EST 1 1 0 0 0

HAUTS BASSINS 2 1 0 0 0

NORD 2 2 0 0 0

SAHEL 2 2 0 0 0

SUD OUEST 1 1 0 0 0 Source: IMPAQ computations based on household data, Section F (Questions F01, F02A-D)


Table 14 Mode of field transfer for fields used for agricultural purposes

Région N %

Officially sold

through land

institution

Unofficially sold

between buyer and

seller

Lost b/c of land

conflicts

Government expropriation

Donated Other Total

BOUCLE DU MOUHOUN N 0 0 3 1 2 1 7 % 0 0 42.86 14.29 28.57 14.29 100

CASCADES N 0 1 7 3 2 2 15 % 0 6.67 46.67 20 13.33 13.33 100

CENTRE N 1 3 6 5 6 0 22

% 4.55 13.64 27.27 22.73 27.27 0 100

CENTRE EST N 1 0 4 1 7 3 16

% 6.25 0 25 6.25 43.75 18.75 100

CENTRE NORD N 1 0 0 1 3 2 7

% 14.29 0 0 14.29 42.86 28.57 100

CENTRE N 0 3 6 2 12 2 26

% 0 11.54 23.08 7.69 46.15 7.69 100

CENTRE SUD N 0 1 3 1 14 3 22 % 0 4.55 13.64 4.55 63.64 13.64 100

EST N 0 0 1 0 0 0 1 % 0 0 100 0 0 0 100

HAUTS BASSINS N 0 0 19 1 21 2 43

% 0 0 44.19 2.33 48.84 4.65 100

NORD N 0 0 0 1 0 2 3

% 0 0 0 33.33 0 66.67 100

SAHEL N 0 0 1 0 5 2 8

% 0 0 12.5 0 62.5 25 100

SUD OUEST N 0 0 1 0 0 0 1

% 0 0 100 0 0 0 100

Total N 3 8 51 16 72 19 171 % 1.75 4.68 29.82 9.36 42.11 11.11 100

Source: IMPAQ computations based on household data, Section F (Questions F03)


3. Field Manager Questionnaire

3.1 Types of land fields

The first section of the field manager questionnaire asked to identify the types of fields managed by the field manager and their main use. Fields are classified according to whether they belong to the field manager (or any other member of the household) or whether they are held with some type of user rights. The first panel in the table indicates whether the field belong to the household or one of the members, or whether is held with some types of user rights. The results in Table 15 show that the majority of fields belong to the household (76%). The table also shows that the primary use for most fields (72%) is for annual crops, followed by use of the field as primary residence of the household (23%). The results also show that approximately 50% of fields are of 1Ha or less in size, and most fields are also located in the same village where the household lives (95%).

Table 15: Characteristics of land fields

Property or User Rights N %

Field belongs to the household or one of the members 12,504 76.36

Field held with user rights 3,872 23.64

Total 16,376 100

Primary use of the field in the last 12 months N %

Residence of the household 3,765 23

Annula crops 11,787 71.99

Permanent crops 167 1.02

Pastures (livestock) 73 0.45

Commercial activities 143 0.87

Left fallow 268 1.64

Rented/loaned outside the household 169 1.03

Total 16,372 100

Area of the field in Hectares N % Less than ¼ HA 2,480 15.16

[¼ - ½ HA) 2,605 15.92

[ ½ - 1 HA) 3,506 21.43

[1- 2 HA) 3,465 21.18

[2 - 3 HA) 1,682 10.28

[3 - 4 HA) 930 5.68

[4 - 5 HA) 525 3.21

[5 - 7 HA) 571 3.49

[7 - 10 HA) 227 1.39

[10 - 15 HA) 199 1.22

[15 - 20 HA) 83 0.51

20 HA + 90 0.55

Total 16,363 100

Location of the field N %


Same village where the HH livese 15,348 94.06

Different village, same commune 829 5.08

Different commune, same province 91 0.56

Other province 50 0.31

Total 16,318 100 Source: IMPAQ computations based on field manager data, Section H (Questions H04, H05, H09)

The results also show (Table 16) that most of the fields that belong (owned) to the household have been acquired by inheritance (56%) or obtained for free/as gift (30%). However for the vast majority of these fields (97%) there is no document attesting the property right on the land.

Table 16: Acquisition and land titles on the fields owned by the household

Acquisition method N %

Buy 412 3.3

Allocated by the community/local authorites 788 6.31

Inherited 6,961 55.77

Obtained for free/gift 3,774 30.24

Occupied 495 3.97

Other 52 0.42

Total 12,482 100

Document attesting ownership N %

No title 12,062 96.67

Occupation permit 214 1.72

Exploitation permit 38 0.3

Land title 41 0.33

Urban permit 71 0.57

Other 52 0.42

Total 12,478 100 Source: IMPAQ computations based on field manager data, Section I (Questions I02 I07)

Regarding the fields that are held with user rights, the data show that most of the user rights on the fields have been obtained from someone outside the household (62%), or from another household member (28%). In addition almost 57% of fields have been held with user rights for more than 5 years (Table 17).


Table 17: Fields held with user rights

Acquisition method of user rights N %

Obtained from another household members 1,078 27.86

Obtained from the family of origin (females) 262 6.77

Obtained from persons outside the HH 2,418 62.5

Other 111 2.87

Total 3,869 100

Length of time user rights have been held N %

Less than one year 560 14.47

1 to 5 years 1,110 28.69

5 years or more 2,196 56.76

Do not know 2 0.05

Total 3,868 100 Source: IMPAQ computations based on field manager data, Section J (Questions J02 J03)

3.2 Perception of Land Security and land conflict on the fields

The next set of tables describes information obtained from the field manager questionnaire. In particular, Table 18 describes individual perceptions about fear of loss of land because of various reasons and by gender of the field manager. The data show that across all regions, a great proportion of individuals fear of losing their land because of government expropriation, with men being somewhat more likely to fear the loss than women. A large proportion of individual also fear losing their land because of lack of formal documents attesting property or user rights. There are substantial regional differences: for example 28% (26%) of men (female) fear to lose their land because of lack of land titles in the NORD region, but the percentage are 56% (46%) in the south of the country (CASCADE region).


Table 18 Fear of losing land for various reasons

Whether you fear of losing the field because :

Government Expropriation

Expropriation by other villagers

Lack of documents

proving ownership/user

rights

Other

Sex Sex Sex Sex

Region Male Female Male Female Male Female Male Female OUCLE DU MOUHOUN 69% 62% 25% 30% 52% 44% 3% 6%

CASCADES 60% 57% 41% 37% 56% 46% 3% 2%

CENTRE 65% 58% 31% 43% 45% 49% 7% 6%

CENTRE EST 50% 45% 31% 27% 48% 38% 8% 6%

CENTRE NORD 56% 54% 22% 25% 53% 50% 1% 8%

CENTRE 57% 57% 20% 21% 46% 39% 3% 4%

CENTRE SUD 55% 55% 25% 23% 47% 41% 3% 7%

EST 48% 21% 31% 42% 40% 13% 2% 3%

HAUTS BASSINS 70% 72% 49% 49% 67% 64% 7% 10%

NORD 43% 31% 13% 12% 28% 26% 2% 1%

SAHEL 68% 56% 17% 11% 57% 50% 33% 44%

SUD OUEST 48% 48% 10% 24% 41% 48% 0% 10%

Source: IMPAQ computations based on field manager data, Section M (Questions M01A-D)

Table 19 shows the percentage of fields that experiences a land conflict: the data indicate that 97.4% of land fields did not have any conflicts on it.

Table 19: Fields with land conflicts

Whether there was a conflict on the field N %

Yes 429 2.63

No 15,875 97.37

Total 16,304 100 Source: IMPAQ computations based on field manager data, Section M (Questions M03)

As described in Table 20, the majority of conflicts on the field (55% across all regions) involve other villagers; Table 21 indicates that the main cause of conflict is related to the borders of the field (34%). In addition, in almost 36% of cases the conflict has been mediated informally between the parties involved, and in other 37% of cases through traditional authorities (see Table 22).


Table 20 Parties Involved in the Conflict

Région N %

Other HH

member

Neighbors

Traditional

authorities

Populaion outside village

Government

Other Total

BOUCLE DU MOUHOUN

N 1 11 9 6 0 1 28 % 3.57 39.29 32.14 21.43 0 3.57 100

CASCADES N 9 27 11 6 2 3 58 % 15.52 46.55 18.97 10.34 3.45 5.17 100

CENTRE N 1 14 0 12 1 10 38

% 2.63 36.84 0 31.58 2.63 26.32 100

CENTRE EST N 1 32 2 6 1 2 44

% 2.27 72.73 4.55 13.64 2.27 4.55 100

CENTRE NORD N 3 6 1 2 0 0 12

% 25 50 8.33 16.67 0 0 100

CENTRE N 5 25 11 1 2 1 45

% 11.11 55.56 24.44 2.22 4.44 2.22 100

CENTRE SUD N 1 23 9 9 0 0 42 % 2.38 54.76 21.43 21.43 0 0 100

EST N 5 7 0 3 0 3 18 % 27.78 38.89 0 16.67 0 16.67 100

HAUTS BASSINS N 7 70 12 14 4 10 117

% 5.98 59.83 10.26 11.97 3.42 8.55 100

NORD N 0 7 1 0 0 0 8

% 0 87.5 12.5 0 0 0 100

SAHEL N 0 5 0 0 0 0 5

% 0 100 0 0 0 0 100

SUD OUEST N 1 1 0 0 0 0 2

% 50 50 0 0 0 0 100

Total N 34 228 56 59 10 30 417 % 8.15 54.68 13.43 14.15 2.4 7.19 100

Source: IMPAQ computations based on field manager data, Section M (Questions M07)


Table 21 Cause of last Conflict

Région

N %

Inheritance

Ownership (other mattters

)

User right

s

Borders of the

field

Probelms with livestoc

k

Expropriation by Gvnmnt

Other Total

BOUCLE DU MOUHOUN

N 1 4 3 12 8 0 0 28 % 3.57 14.29 10.71 42.86 28.57 0 0 100

CASCADES N 2 8 5 35 0 1 7 58 % 3.45 13.79 8.62 60.34 0 1.72 12.07 100

CENTRE N 5 8 6 6 11 2 0 38

% 13.16 21.05 15.79 15.79 28.95 5.26 0 100

CENTRE EST N 6 23 3 7 3 1 1 44

% 13.64 52.27 6.82 15.91 6.82 2.27 2.27 100

CENTRE NORD N 3 1 1 7 0 0 0 12

% 25 8.33 8.33 58.33 0 0 0 100

CENTRE N 6 8 10 11 10 1 0 46

% 13.04 17.39 21.74 23.91 21.74 2.17 0 100

CENTRE SUD N 4 2 12 9 14 0 1 42 % 9.52 4.76 28.57 21.43 33.33 0 2.38 100

EST N 6 1 3 5 1 0 2 18 % 33.33 5.56 16.67 27.78 5.56 0 11.11 100

HAUTS BASSINS N 23 23 11 45 9 5 1 117

% 19.66 19.66 9.4 38.46 7.69 4.27 0.85 100

NORD N 3 1 1 1 2 0 0 8

% 37.5 12.5 12.5 12.5 25 0 0 100

SAHEL N 0 2 0 3 0 0 0 5

% 0 40 0 60 0 0 0 100

SUD OUEST N 1 0 0 1 0 0 0 2

% 50 0 0 50 0 0 0 100

Total N 60 81 55 142 58 10 12 418 % 14.35 19.38 13.16 33.97 13.88 2.39 2.87 100

Source: IMPAQ computations based on field manager data, Section M (Q M09)


Table 22 Institution mediating the conflict

Mediation Entity

Région N %

Informal

Traditional

authorities

cvd/ccfv/ccfc

Police Departme

ntal tribunal

TGI Other Total

BOUCLE DU MOUHOUN

N 9 10 5 1 0 1 2 28 % 32.14 35.71 17.86 3.57 0 3.57 7.14 100

CASCADES N 16 30 3 3 3 0 3 58 % 27.59 51.72 5.17 5.17 5.17 0 5.17 100

CENTRE N 14 17 0 3 0 0 2 36

% 38.89 47.22 0 8.33 0 0 5.56 100

CENTRE EST N 10 25 2 3 4 0 0 44

% 22.73 56.82 4.55 6.82 9.09 0 0 100

CENTRE NORD N 5 3 0 0 2 0 0 10

% 50 30 0 0 20 0 0 100

CENTRE N 17 12 6 7 1 0 2 45

% 37.78 26.67 13.33 15.56 2.22 0 4.44 100

CENTRE SUD N 16 7 12 4 1 0 2 42 % 38.1 16.67 28.57 9.52 2.38 0 4.76 100

EST N 13 2 2 0 1 0 0 18 % 72.22 11.11 11.11 0 5.56 0 0 100

HAUTS BASSINS N 45 46 14 2 4 3 3 117

% 38.46 39.32 11.97 1.71 3.42 2.56 2.56 100

NORD N 4 1 0 0 2 1 0 8

% 50 12.5 0 0 25 12.5 0 100

SAHEL N 2 1 0 1 0 1 0 5

% 40 20 0 20 0 20 0 100

SUD OUEST N 0 1 1 0 0 0 0 2

% 0 50 50 0 0 0 0 100

Total N 151 155 45 24 18 6 14 413 % 36.56 37.53 10.9 5.81 4.36 1.45 3.39 100


The last two tables relative to this section of the field manger questionnaire shows that for a large proportion of cases (42%) the conflict affected the activity on the land (Table 23), and in approximately 30% of cases the respondents are worried to have a conflict on their field one day (Table 24).


Table 23 Conflict affects activity on the field

Region N %

Yes Non Total

BOUCLE DU MOUHOUN N 12 16 28 % 42.86 57.14 100

CASCADES N 27 31 58

% 46.55 53.45 100

CENTRE N 15 23 38

% 39.47 60.53 100

CENTRE EST N 14 16 30

% 46.67 53.33 100


% 16.67 83.33 100

CENTRE N 14 32 46 % 30.43 69.57 100

CENTRE SUD N 18 24 42 % 42.86 57.14 100

EST N 5 13 18

% 27.78 72.22 100


% 50.86 49.14 100

NORD N 3 5 8

% 37.5 62.5 100

SAHEL N 1 4 5

% 20 80 100

SUD OUEST N 0 2 2 % 0 100 100

Total N 170 233 403 % 42.18 57.82 100



Table 24 Whether the respondent is worried to be in conflict on the field

Region

N %

Yes No Do not

know Total

BOUCLE DU MOUHOUN N 381 1,043 11 1,435 % 26.55 72.68 0.77 100

CASCADES N 754 846 5 1,605 % 46.98 52.71 0.31 100

CENTRE N 300 754 4 1,058

% 28.36 71.27 0.38 100

CENTRE EST N 471 1,284 1 1,756

% 26.82 73.12 0.06 100

CENTRE NORD N 218 691 0 909

% 23.98 76.02 0 100

CENTRE N 764 2,693 12 3,469

% 22.02 77.63 0.35 100

CENTRE SUD N 486 1,331 13 1,830 % 26.56 72.73 0.71 100

EST N 162 325 0 487 % 33.26 66.74 0 100

HAUTS BASSINS N 1,043 1,119 8 2,170

% 48.06 51.57 0.37 100

NORD N 119 756 4 879

% 13.54 86.01 0.46 100

SAHEL N 141 309 3 453

% 31.13 68.21 0.66 100

SUD OUEST N 24 183 0 207

% 11.59 88.41 0 100

Total N 4,863 11,334 61 16,258 % 29.91 69.71 0.38 100


3.3 Commercialization of Agricultural Production

Section O of the field manager questionnaire describes agricultural revenues by crop. Table 25 describes the top 10 crops sold by male and female field managers. The results indicate that most frequent crop sold is Cotton for men, followed by Sesame, Corn, Sorghum, Cowpea and Millet. For women, the most frequent crop reported to be sold is Peanuts, followed by Cowpea, Sesame, Rice and Sorgum. Table 26 then lists the average revenues for the 10 crops sold by males and female field managers.


Table 25 Crops sold by Gender

Top crops sold by Gender

Crop Men Crop Female

Cotton 889 Peanuts 1,354

Sésame 693 Cowpea 584

Corn 688 Sésame 437

Sorghum 600 Rice 407

Cowpea 555 Sorghum 291

Peanuts 543 Ground Nut 225

Millet 292 Millet 202

Other 255 Gombos 196

Rice 208 Corn 133

Onions 110 Cotton 101

Cabbage 64 Other 100

Tomatos 55 Soia 62

Ground Nut 54 Peppers 37

Soia 52 Onions 28

Yam 41 Tomatos 23

Watermelons 31 Peas 15

Igname 28 Green beans 11

Peppers 27 Cabbage 9

Gombos 26 Chick pea 7

Green beans 24 Yam 5

Cucumbers 14 Cucumbers 4

Peas 13 Tobacco 4

Tobacco 13 Salad 4

Salad 12 Watermelons 2

Cassava 12 Spinach 2

Potatos 4 Sqaush 2

Carrots 3 Potatos 1

Chick pea 3 Igname 0

Spinach 2 Cassava 0

Sqaush 2 Carrots 0

Ananas 2 Ananas 0

Grain 1 Grain 0

Total 5,316 Total 4,246 Source: IMPAQ computations based on field manager data, Section O (Question O01B)


Table 26 Revenues from top 10 crops for male/female field managers

Crop N Mean SD

Male field managers

cotton 884 602325.3 961192.8

sesame 692 160459 458503.7

corn 685 231572.9 499593.3

sorghums 599 94930.63 304947.1

cowpea 553 49213.47 84977.82

peanuts 541 77985.77 109734.9

millet 290 65376.64 93465.85

rice 203 723793.8 7167890

onions 110 335590.9 618917.8

cabbage 64 104757.8 141678.5

Female field managers

peanuts 1347 27677.04 34240.6

cowpea 584 18585.06 37747.73

sesame 438 39456.39 50542.44

rice 406 39196.54 56165.6

sorghums 288 31339.76 69422.47

groundnut 223 16867.04 24475.75

millet 200 23044.75 40050.4

gombos 194 14441.88 24922.89

corn 133 90714.29 244094.7

cotton 101 155134.9 427267.6 Source: IMPAQ computations based on field manager data, Section O

3.4 Perception of gender inequality in access to land

Section T of the field manager questionnaire is addressed to female field managers and other female decision makers in the household to capture women’s perception about the issue of inequality in access to land for the women in the village. Table 27 indicates the number and percentage of respondents who declare that women of the village can access land (i) without restrictions, (ii) with household approval, (iii) with family approval, (iv) with approval from the village chief or (v) cannot access at all. The results show that most respondents (45%) believe that women do not have access to land (i.e. they do not have the right to acquire or own land). A large percentage of respondents also indicate that they can access land only after having approval of the household. Only 8% of respondents declare that land can be accessed without any restriction. Results tend to vary by region, where up to 17% of women declare that females have free access to land in the CENTRE.


Table 27 Whether women can access land (acquire or own land)

Region N %

No restrictions

Household approval

Family approval

Land chief

approval

No access

Total

BOUCLE DU MOUHOUN N 34 112 15 60 183 404

% 8.42 27.72 3.71 14.85 45.3 100

CASCADES N 35 212 43 75 244 609

% 5.75 34.81 7.06 12.32 40.07 100

CENTRE N 77 201 37 37 102 454

% 16.96 44.27 8.15 8.15 22.47 100

CENTRE EST N 44 220 53 22 209 548 % 8.03 40.15 9.67 4.01 38.14 100

CENTRE NORD N 8 87 8 13 192 308

% 2.6 28.25 2.6 4.22 62.34 100

CENTRE N 68 342 96 44 676 1,226

% 5.55 27.9 7.83 3.59 55 100

CENTRE SUD N 46 199 32 29 222 528

% 8.71 37.69 6.06 5.49 42.05 100

EST N 7 30 6 2 115 160

% 4.38 18.75 3.75 1.25 71.88 100

HAUTS BASSINS N 95 255 25 48 293 716

% 13.27 35.61 3.49 6.7 40.92 100

NORD N 22 129 19 3 101 274 % 8.03 47.08 6.93 1.09 36.86 100

SAHEL N 6 27 13 2 95 143

% 4.2 18.88 9.09 1.4 66.43 100

SUD OUEST N 2 21 1 21 36 81

% 2.47 25.93 1.23 25.93 44.44 100

Total N 444 1,835 348 356 2,468 5,451

% 8.15 33.66 6.38 7 45 100 Source: IMPAQ computations based on field manager data, Section T (question T02)

The next tables describe respondents’ opinion about whether women of the village can decide about the use of land (i) without restrictions, (ii) with household approval, (iii) with family approval, (iv) with approval from the village chief or (V) cannot decide at all. The results in Table 28 indicate that 37.6% of respondents believe women in the village can decide about the use of land only after having household approval, while 32% believe women cannot freely decide about land use.


Table 28 Whether women can decide about use of land

Région N %

No restrictions

Household approval

Family approval

Land chief approval

Cannot decide

Total

BOUCLE DU MOUHOUN N 77 122 18 46 141 404

% 19.06 30.2 4.46 11.39 34.9 100

CASCADES N 71 252 51 42 193 609 % 11.66 41.38 8.37 6.9 31.69 100

CENTRE N 100 239 33 18 64 454 % 22.03 52.64 7.27 3.96 14.1 100

CENTRE EST N 94 231 53 23 147 548 % 17.15 42.15 9.67 4.2 26.82 100

CENTRE NORD N 93 123 11 4 77 308

% 30.19 39.94 3.57 1.3 25 100

CENTRE N 233 378 94 27 494 1,226

% 19 30.83 7.67 2 40 100

CENTRE SUD N 139 172 28 15 174 528

% 26.33 32.58 5.3 2.84 32.95 100

EST N 22 43 9 0 86 160 % 13.75 26.88 5.63 0 53.75 100

HAUTS BASSINS N 189 298 30 10 189 716 % 26.4 41.62 4.19 1.4 26.4 100

NORD N 86 117 12 0 59 274 % 31.39 42.7 4.38 0 21.53 100

SAHEL N 7 50 6 0 80 143

% 4.9 34.97 4.2 0 55.94 100

SUD OUEST N 5 23 9 9 35 81

% 6.17 28.4 11.11 11.11 43.21 100

Total N 1,116 2,048 354 194 1,739 5,451

% 20.47 37.57 6 4 32 100

Source: IMPAQ computations based on field manager data, Section T (question T03)

Similar questions are asked about the women of the village can take decisions related to the household or the land (Table 29 and 30). The results show that according to most respondents, women freedom to make those decisions without any restriction is limited. For example, only 15% of respondent indicate that women can decide without any restrictions about household matters, and only 6% can freely decide about matters related to the land (Table 30).


Table 29 Whether women can make decisions about the household

Région

N %

No restrictions

Household approval

Family approval

Land chief approval

Cannot decide

Total

BOUCLE DU MOUHOUN N 36 230 24 21 93 404 % 8.91 56.93 5.94 5.2 23.02 100

CASCADES N 79 369 38 6 117 609 % 12.97 60.59 6.24 0.99 19.21 100

CENTRE N 161 232 21 5 35 454

% 35.46 51.1 4.63 1.1 7.71 100

CENTRE EST N 57 311 26 11 143 548

% 10.4 56.75 4.74 2.01 26.09 100

CENTRE NORD N 46 203 9 1 49 308

% 14.94 65.91 2.92 0.32 15.91 100

CENTRE N 156 582 117 3 368 1,226

% 12.72 47.47 9.54 0 30 100

CENTRE SUD N 103 334 26 2 63 528 % 19.51 63.26 4.92 0.38 11.93 100

EST N 14 80 16 0 50 160 % 8.75 50 10 0 31.25 100

HAUTS BASSINS N 143 335 21 1 216 716

% 19.97 46.79 2.93 0.14 30.17 100

NORD N 22 184 20 0 48 274

% 8.03 67.15 7.3 0 17.52 100

SAHEL N 14 89 11 0 29 143

% 9.79 62.24 7.69 0 20.28 100

SUD OUEST N 9 57 0 0 15 81

% 11.11 70.37 0 0 18.52 100

Total N 840 3,006 329 50 1,226 5,451 % 15.41 55.15 6 1 22 100

Source: IMPAQ computations based on field manager data, Section T (question T06)


Table 30: Whether women can make decisions related to land

Région No

restrictions Household approval

Family approval

Land chief approval

Cannot decide

Total

BOUCLE DU MOUHOUN 26 84 24 48 222 404

6.44 20.79 5.94 11.88 54.95 100

CASCADES 23 165 59 53 309 609

3.78 27.09 9.69 8.7 50.74 100

CENTRE 41 160 34 41 178 454

9.03 35.24 7.49 9.03 39.21 100

CENTRE EST 49 145 40 22 292 548

8.94 26.46 7.3 4.01 53.28 100

CENTRE NORD 9 109 18 11 161 308

2.92 35.39 5.84 3.57 52.27 100

CENTRE 53 221 82 55 815 1,226

4.32 18.03 6.69 4 66.48 100

CENTRE SUD 45 117 42 15 309 528

8.52 22.16 7.95 2.84 58.52 100

EST 2 36 11 2 109 160

1.25 22.5 6.88 1.25 68.13 100

HAUTS BASSINS 68 157 49 20 422 716

9.5 21.93 6.84 2.79 58.94 100

NORD 19 73 17 6 159 274

6.93 26.64 6.2 2.19 58.03 100

SAHEL 6 20 12 0 104 142

4.23 14.08 8.45 0 73.24 100

SUD OUEST 2 10 4 18 47 81

2.47 12.35 4.94 22.22 58.02 100

Total 343 1,297 392 291 3,127 5,450

6.29 23.8 7 5 57.38 100


APPENDIX 3: DATA QUALITY CHECKS

In this section we describe some data quality checks performed on the data. In particular, we performed the following data quality checks:

a) Whether the variables have codes and labels consistent with the questionnaires.

Our review of selected data shows a general consistency between the datasets and

the questionnaires/codebook.

b) Whether variables are consistent with the skip patterns described in the questionnaire

(for example, marital status question should be asked only to household members age

10 and above).

Our review of selected data shows a general consistency with the skip patterns

among variables within a given dataset.

c) Whether the number of missing values on the variables is reasonable, after having taken

into account the skip pattern of the data

Our review of selected variables shows that the number of missing varies by

variable, which is generally low for variables like basic demographic characteristics

and few other selected variables. Some detailed variables are presented below in

Table 30-31.

d) We also verified the consistency among some key datasets that serves as screen within

and across modules. For example, the field manager roster serves as to identify field

managers to administer the field manager questionnaire. Also, fields are distinguished

based on whether they are owned by the household or just held with user rights. We

checked whether the appropriate types of fields are included in the relevant sections

(e.g. whether section I is addressed only to fields that are owned).

Our review of selected datasets reveals that the number of inconsistencies across

data modules is generally low.

The next set of tables is structured as follows: for some of the key variables in the questionnaire we report the total number of records and the number and percentage with missing values. Give the large amount of data and variables, we focused on a limited number of sections for the household, field manager and village questionnaire.

The results of the tables show that, for variables reported from the household questionnaire the amount of missing is generally very low. For the field manager questionnaire, the results of


the tabulations show that the number of missing is generally low. One exception is the case of variables capturing the area of the field in hectares. In 52% of cases the individual is not able to report the exact amount of land, but when s/he cannot report the exact area s/he can respond to the same question where suggested land area brackets are proposed (Table 31). This is true for both the area reported for the parcel as a whole (Section H), and for the area under production for each crop (section N). This will limit our capacity to compute accurate yields for the various crops. The last table analyzes the incidence of missing values on the section of the village questionnaire describing the number of conflicts reported to the various authorities. The number of missing is somewhat higher on some key variables (e.g. number of conflicts reported to the various authorities).

Table 31: Missing values on selected variables, household questionnaire

Household Questionnaire

Section B: characteristics of the household members

Sex N %

Missing 0 0

Total 32,594 100

Age N %

Missing 3 0.01

Total 32,594 100

Relation to Household head N %

Missing 12 0.04

Total 32,594 100

Marital status N %

Missing 81 0.42

Total 19,392 100

Can read/write N %

Missing 42 0.25

Total 16,741 100

Attended school N %

Missing 107 0.64

Total 16,741 100

Highest grade achieved N %

Missing 4 0.12

Total 3,435 100

Attended school in 2012-2013 N %

Missing 21 0.61

Total 3,435 100

Highest grade in 2012-2013 N %

Missing 0 0


Note: the number of missing does not include missing due to appropriate skip patterns Source: IMPAQ computations using field manager data

Total 1,311 100

Section E: Identification of field managers

Field manager ID N %

Missing 0 0

Total 8,096 100

Number of fields managed by the field manager N %

Missing 0 0

Total 8,096 100

Number of fields belonging to the field manager N %

Missing 0 0

Total 8,096 100

Number of fields that do not belong to the field manager N %

Missing 0 0

Total 8,096 100

Field manager is the respondent N %

Missing 2 0.02

Total 8,096 100

Respondent ID N %

Missing 14 0.17

Total 8,096 100


Table 32: Missing values on selected variables, field manager questionnaire

Field Manager Questionnaire

Section H: Identification of all the Fields Section I: Fields Owned

Field manager is the respondent N % Id of the HH member owning the field N %


Total 16,376 100 Total 12,504 100

Respondent ID N % Acquisition method of ownership on the field N %


Total 1,665 100 Total 12,504 100

Ownership or user right on the field N % When ownership was acquired N %

Missing 0 0 Missing 92 0.74

Total 16,376 100 Total 12,504 100

Primary use of the field N % Time to obtain ownership N %


Total 16,376 100 Total 12,504 100

Secondary Use of the field N % Money spend to get ownership of the field N %


Total 16,376 100 Total 12,504 100

Area in Ha N % Transaction cost to obtain ownership N %

Missing 8,474 51.75 Missing 1,398 11.18

Total 16,376 100 Total 12,504 100

Area in Ha (intervals) N % Estimated value of the field if it were sold N %

Missing 13 0.15 Missing 2,042 16.33

Total 8,474 100 Total 12,504 100

Distance between the field and the house N %

Type of document attesting ownership N %


Total 16,376 100 Total 12,504 100

Geographic location of the field N % ID of the HH member on the document N %


Total 16,376 100 Total 416 100

Topography of the field N % Who delivered the document N %


Total 16,376 100 Total 416 100

Type of land in the field N % Why did you obtain a document for the field? N %



Field Manager Questionnaire

Total 16,376 100 Total 416 100

Section J: Fields with User rights Section N and O: Agr. Production

Why there is no official document N % Area in ha N %

Missing 127 1.05 Missing 13,391 53.94

Total 12,062 100 Total 24,826 100

Whether the field has been rented out N % Area in ha (intervals) N %


Total 12,504 100 Total 13,443 100

Rent amount N % Amount of production harvested (in local units) N %


Total 217 100 Total 24,826 100

Id of the HH member with user rights N % Local unit measure N %

Missing 0 0 Missing 85 0.35

Total 3,872 100 Total 24,326 100

Acquisition method of user right N % Sold the crop N %


Total 3,872 100 Total 21,712 100

Length of possession of user right N % Quantity sold (ULM) N %


Total 3,872 100 Total 9,562 100

Amount paid in the last 12 months for the use of the field N % Local unit measure N %


Total 3,872 100 Total 9,519 100

Note: missing values do not account for missing due to skip patterns Source: IMPAQ computations using field manager data Agricultural Revenues N %

Missing 45 0.47

Total 9,562 100


Table 33 Missing values on selected variables, village questionnaire

Village Questionnaire Section F

Whether village has authority for conflict resolution N %

Missing 33 0.93

Total 3,531 100

Year of establishment of the authority N %

Missing 407 16.88

Total 2,411 100

Whether authority is consulted in case of land conflict N %

Missing 48 1.99

Total 2,411 100

Number of conflicts reported to the authority N %

Missing 188 7.8

Total 2,411 100

Main cause of conflicts reported N %

Missing 70 8.44

Total 829 100

Number of conflicts solved N %

Missing 36 4.34

Total 829 100 Note: missing values do not account for missing due to skip patterns Source: IMPAQ computations using village data

Regarding the commune level questionnaire, some sections have a large number of missing. For example, Section E asks the respondents to indicate the villages belonging to the commune and the land area of the village. The results indicated that 26% of records have missing information about the code of the village in the commune, and 84% of records have missing information about the land area of the village.

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