The Côte d'Ivoire Living Standards Survey (CILSS) was the first LSMS Survey to have field tested the methodology and questionnaire developed by LSMS. It consists of three complementary surveys: the household survey, the community survey and the price survey. The household survey collected detailed information on expenditures, income, employment, assets, basic needs and other socio-economic characteristics of the households. The Community Survey collected information on economic and demographic characteristics of the rural communities to which each cluster of households belonged. This was designed to enable the linkage of community level with household level data. The price survey component of the CILSS collected data on prices at the nearest market to each cluster of households, so that regional price indices could be constructed for the household survey.

The Côte d'Ivoire Living Standards Survey (CILSS) was undertaken over a period of four years, 1985-88, by the Direction de la Statistique in Côte d'Ivoire, with financial and technical support from the World Bank during the first two years of the survey. It was the first year-round household survey to have been undertaken by the Ivorian Direction de la Statistique.

The sample size each year was 1600 households and the sample design was a rotating panel. That is, half of the households were revisited the following year, while the other half were replaced with new households. The survey thus produced four cross-sectional data sets as well as three overlapping panels of 800 households each (1985-86, 1986-87, 1987-88).

Abstract

The Côte d'Ivoire Living Standards Survey (LSS) was the first LSMS Survey to have field tested the methodology and questionnaire developed by LSMS. It consists of three complementary surveys: the household survey, the community survey and the price survey. The household survey collected detailed information on expenditures, income, employment, assets, basic needs and other socio-economic characteristics of the households. The Community Survey collected information on economic and demographic characteristics of the rural communities to which each cluster of households belonged. This was designed to enable the linkage of community level with household level data. The price survey component of the CILSS collected data on prices at the nearest market to each cluster of households, so that regional price indices could be constructed for the household survey. The Côte d'Ivoire Living Standards Survey (LSS) was undertaken over a period of four years, 1985-88, by the Direction de la Statistique in Côte d'Ivoire, with financial and technical support from the World Bank during the first two years of the survey. It was the first year-round household survey to have been undertaken by the Ivorian Direction de la Statistique. The sample size each year was 1600 households and the sample design was a rotating panel. That is, half of the households were revisited the following year, while the other half were replaced with new households. The survey thus produced four cross-sectional data sets as well as three overlapping panels of 800 households each (1985-86, 1986-87, 1987-88).

Geographic coverage

National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLE DESIGN The principal objective of the sample selection process for the LSS Household Survey was to obtain a nationally representative cross-section of African households, some of which could be interviewed in successive years as panel households. A two-stage sampling procedure was used. In the first stage, 100 Primary Sampling Units (PSUs) were selected across the country from a list of all PSUs available in the sampling frame. At the second stage, a cluster of 16 households was selected within each PSU. This led to a sample size of 1600 households a year, in 100 cluster s of 16 households each. Half of the households were replaced each year while the other half (the panel households in 1986, 1987 and 1988) were interviewed a second time. It is important to note that there was a change in the sampling procedures (the sampling frame, PSU selection process and listing procedures), used to select half of the clusters/households interviewed in 1987 (the other half were panel households retained from 1986), and all of the clusters/households interviewed in 1988. Households selected on the basis of the first set of sampling procedures will henceforth be referred to as Block 1 data while households based on the second set of sampling procedures will be referred to as Block 2 data.

(b) SAMPLE FRAME 1. Sampling Procedures for Block 1 Data The Sampling Frame. The sampling frame for the 1985, 1986, and half of the 1987 samples (except for Abidjan and Bouaké) was a list of localities constructed on the basis of the 1975 Census, updated to 1983 by the demographers of the Direction de la Statistique and based on a total population estimated at 9.4 million in 1983.The Block 1 frame for Abidjan and Bouaké was based on data from a 1979-80 electoral census of these two cities. The electoral census had produced detailed maps of the two cities that divided each sector of the city into smaller sub-sectors (îlots). Sub-sectors with similar types of housing were grouped together by statisticians in the Direction de la Statistique to form PSUs. From a list of all PSUs in each city, along with each PSU's population size, the required number of PSUs were selected using a systematic sampling procedure. The step size was equal to the city's population divided by the number of PSUs required in each city. One problem identified in the selection process for Abidjan arose from the fact that one sector of the city (Yopougon) which had been relatively small in 1980 at the time of the electoral census, had since become the largest agglomeration in Côte d'Ivoire. This problem was presumably unavoidable since accurate population data for Yopougon was not available at the time of the PSU selection process.

Selection of PSUs. Geographic stratification was not explicitly needed because the systematic sampling procedure that was used to select the PSUs ensured that the sample was balanced with respect to region and by site type, within each region. The main geographical regions defined were: East Forest, West Forest, and Savannah. Site types varied as follows: large cities, towns, large and small villages, surrounding towns, village centers, and villages attached to them. The 100 PSUs were selected, with probabilities proportional to the size of their population, from a list of PSUs sorted by region and within each region, by site type. Selection of households within each PSU. A pre-survey was conducted in June-July of 1984, to establish the second-stage sampling frame, i.e. a list of households for each PSU from which 16 households could be selected. The same listing exercise was to be used for both the 1985 and 1986 surveys, in order to avoid having to conduct another costly pre-survey in the second year. Thus, the 1984 pre-survey had to provide enough households so as to be able to select two clusters of households in each PSU and to allow for replacement households in the event that some in the sample could not be contacted or refused to participate. A listing of 64 households in each PSU met this requirement. In PSUs with 64 households or fewer, every household was listed. In selecting the households, the "step" used was equal to the estimated number of households in the PSU divided by 64. For example, if the PSU had an estimated 640 households, then every tenth household was included in the listing, counted from a random starting point in the PSU. For operational reasons, the maximum step allowable was a step of 30. In practice, it appears that enumerators used doors, instead of housing structures, in counting the step. Al though enumerators were supposed to start the listing process from a random point in the PSU, in rural areas and small towns, reportedly, the lister started from the center of the PSU.

1. Sampling Procedures for Block 2 Data

The Sampling Frame. The sampling frame for Block 2 data was established from a list of places from the results of the Census of inhabited sites (RSH) performed in preparation for the 1988 Population Census. Selection of PSUs. The PSUs were selected with probability proportional to size. However, in order to save what might have been exorbitant costs of listing every household in each selected PSU in a pre-survey, the Direction de la Statistique made a decision to enumerate a smaller unit within each PSU. The area within each PSU was divided into smaller blocks called `îlots'. Households were then selected from a randomly chosen îlot within each PSU. The sample îlot was selected with equal probability within each PSU, not on the basis of probability proportional to size. (These îlots are reportedly relatively small compared with the size of PSUs selected for the Block 1 frame, but no further information is available about their geographical position within the PSUs.) Selection of households within each PSU. All households in each îlot selected for the Block 2 sample were listed. Sixteen households were then randomly chosen from the list of households for each îlot.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

The Household Questionnaire was almost entirely pre-coded, thus reducing errors involved in the coding process. Also, the decentralized data entry system allowed for immediate follow-up on inconsistencies that were detected by the data entry program. Household and personal identification codes were recorded in each section, facilitating merging data across sections

Sampling error estimates

(a) ACCURACY The general consensus is that the quality of the LSS household data is very good. An informal review of data quality conducted by Ainsworth and Mehra (1988) assessed the 1985 and 1986 LSS data in terms of their accuracy, completeness, and internal consistency. The LSS household data were found to score high marks on each of these three counts. One measure of data quality is the extent to which individuals in question respond for themselves during the interview, rather than having proxy responses provided for them by other household members. The investigation of CILSS household survey data for 1985 and 1986 showed that 93 percent of women responded for themselves to the fertility section and that 79 to 80 percent of all adult household members responded for themselves to the employment module. The percent of children responding for themselves to the employment module was far less, 43 to 45 percent. Nevertheless, these rates were found to be higher than for the Peru Living Standards Survey (29 percent).

(b) COMPLETENESS

Investigation of several variables and modules in the LSS (sex, age, parental characteristics, schooling, health, employment, migration, fertility, farming and family business), found that missing data in the household survey are rare. Rates for missing data were found to be close to 0 (0.01 to 0.05 percent) in many cases, but in any case, no higher than 0.76 percent.

Abstract

The objectives of the Smallholder Household Survey in Cote d'Ivoire were to: - Generate a clear picture of the smallholder sector at the national level, including household demographics, agricultural profile, and poverty status and market relationships; - Segment smallholder households in Cote d'Ivoire according to the most compelling variables that emerge; - Characterize the demand for financial services in each segment, focusing on customer needs, attitudes and perceptions related to both agricultural and financial services; and, - Detail how the financial needs of each segment are currently met, with both informal and formal services, and where there may be promising opportunities to add value.

Geographic coverage

National coverage

Analysis unit

Households and individual household members

Universe

The universe for the survey consists of smallholder households defined as households with the following criteria: 1) Household with up to 5 hectares OR farmers who have less than 50 heads of cattle, 100 goats/sheep/pigs, or 1,000 chickens; AND 2) Agriculture provides a meaningful contribution to the household livelihood, income, or consumption.

Kind of data

Sample survey data [ssd]

Sampling procedure

The smallholder household survey in Cote d’Ivoire is a nationally-representative survey, with a target sample size of 3,000 smallholder households. The sample was designed to provide reliable survey estimates at the national level.

Sampling Frame In preparation for the 2014 population census, the country was divided into 22,600 census enumeration areas (EAs). For the ongoing 2015 agricultural census, the National Statistical Office (INS) has identified 18,321 EAs that contain agricultural households. The sampling frame for the smallholder survey is the list of these enumeration areas (EAs) containing agricultural households.

Sample allocation and selection In order to take nonresponse into account, the target sample size was increased to 3,333 households assuming a nonresponse rate of 10%. The total sample size was first allocated to the zones based on their population counts using the power allocation method. Within each zone, the resulting sample was then distributed to urban and rural areas in proportion to their population. Given that EAs were the primary sampling units and 15 households were selected in each EA, a total of 223 EAs were selected. The sample for the smallholder survey is a stratified multistage sample. Stratification was achieved by separating each zone into urban and rural areas. The urban/rural classification is based on the 2014 population census. Therefore, 6 strata were created, and the sample was selected independently in each stratum.

In the first stage, EAs were selected as primary sampling units with probability proportional to size, the size being the population count in the EAs. A household listing operation was conducted in all selected EAs to identify smallholder households and to provide a frame for selecting smallholder households to be included in the sample. In the second stage, 15 smallholders were sampled in each EA with equal probability.

In each sampled household, the household questionnaire was administered to the head of the household, the spouse, or any knowledgeable adult household member to collect information about household characteristics. The multiple respondent questionnaire was administered to all adult members in each sampled household to collect information on their agricultural activities, financial behaviors, and mobile money use. In addition, in each sampled household only one household member was selected using the Kish grid and was administered the single respondent questionnaire.

The full description of the sample design can be found in the user guide for this data set.

Sampling deviation

After the selection of the EAs and the printing of the EA maps, it was necessary to reduce the number of EAs to be listed to 212 for budgetary reasons. Therefore, 212 EAs were randomly selected among the previously 223 sampled EAs and were finally included in the survey sample.

The smallholder survey in Cote d’Ivoire is the fifth survey in the series, following the surveys in Mozambique, Uganda, Tanzania and Bangladesh. Fieldwork in the first countries experienced a lot of failed call backs where identified eligible households and household members could not be interviewed during the time allocated to fieldwork in each country. As a result, the final sample size fell slightly short of the target. For this reason, in Cote d’Ivoire the number of households selected in each EA was increased from 15 to 17 following the household listing operation in all sampled EAs.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

To capture the complexity of smallholder households, the smallholder household survey was divided into three questionnaires: 1) The Household questionnaire; 2) the Multiple Respondent questionnaire; and 3) the Single respondent questionnaire. It was designed in this way to capture the complete portrait of the smallholder household, as some members of the household may work on other agricultural activities independently and without the knowledge of others.

The household questionnaire collected information on the following: • Basic household members’ individual characteristics (age, gender, education attainment, schooling status, relationship with the household head). • Whether each household member contributes to the household income or participates in the household’s agricultural activities. This information was later used to identify all household members eligible for the other two questionnaires. • Household assets and dwelling characteristics.

Both the Multiple and Single Respondent questionnaires collected different information on the following: • Agricultural practices—farm information such as size, crop types, livestock, decision-making, farming association, and markets. • Household economics—employment, income, expenses, shocks, borrowing and saving habits, and investments.

The Single respondent questionnaire also collected the following information: • Mobile phones—attitudes toward phones, use, access, ownership, desire, and importance. • Financial services—attitudes toward financial products and services such as banking and mobile money, including ownership, usage, access and importance.

The questionnaires were translated into French and then pretested. After the pretest, debriefing sessions were held with the pretest field staff and the questionnaires were modified based on the observations from the pretest. After the questionnaires were finalized, a script was developed to support data collection on mobile phones. The script was tested and validated before it was use in the field.

Cleaning operations

The data files were checked for completeness, inconsistencies and errors by InterMedia and corrections were made as necessary and where possible.

Response rate

The user guide to the data set provides detailed tables on household and household member response rates for the Cote d’Ivoire smallholder household survey. A total of 3,415 households were selected for the survey, of which 3,109 were found to be occupied during data collection. Of these, 3,019 were successfully interviewed, yielding a household response rate of 97.1 percent.

In the interviewed households, 6,659 eligible household members were identified for the Multiple Respondent questionnaire. Interviews were completed with 5,706 eligible household members, yielding a response rate of 85.7 percent for the Multiple Respondent questionnaire.

Among the 3,019 eligible household members selected for the Single Respondent questionnaire, 2,949 were successfully interviewed yielding a response rate of 97.7 percent.

Sampling error estimates

The sample design for the smallholder household survey was a complex sample design featuring clustering, stratification and unequal probabilities of selection. For key survey estimates, sampling errors taking into account the design features were produced using either the SPSS Complex Sample module or STATA based on the Taylor series approximation method.

The Côte d'Ivoire Living Standards Survey (CILSS) was the first LSMS Survey to have field tested the methodology and questionnaire developed by LSMS. It consists of three complementary surveys: the household survey, the community survey and the price survey. The household survey collected detailed information on expenditures, income, employment, assets, basic needs and other socio-economic characteristics of the households. The Community Survey collected information on economic and demographic characteristics of the rural communities to which each cluster of households belonged. This was designed to enable the linkage of community level with household level data. The price survey component of the CILSS collected data on prices at the nearest market to each cluster of households, so that regional price indices could be constructed for the household survey. The Côte d'Ivoire Living Standards Survey (CILSS) was undertaken over a period of four years, 1985-88, by the Direction de la Statistique in Côte d'Ivoire, with financial and technical support from the World Bank during the first two years of the survey. It was the first year-round household survey to have been undertaken by the Ivorian Direction de la Statistique. The sample size each year was 1600 households and the sample design was a rotating panel. That is, half of the households were revisited the following year, while the other half were replaced with new households. The survey thus produced four cross-sectional data sets as well as three overlapping panels of 800 households each (1985-86, 1986-87, 1987-88).

Ivory Coast CI: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at 0.740 % in 2015. Ivory Coast CI: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging 0.740 % from Dec 2015 (Median) to 2015, with 1 observations. Ivory Coast CI: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ivory Coast – Table CI.World Bank: Poverty. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Ivory Coast CI: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 10.100 % in 2021. This records a decrease from the previous number of 12.000 % for 2018. Ivory Coast CI: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 13.350 % from Dec 1985 (Median) to 2021, with 12 observations. The data reached an all-time high of 17.400 % in 2008 and a record low of 10.100 % in 2021. Ivory Coast CI: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ivory Coast – Table CI.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Ivory Coast CI: Income Share Held by Highest 20% data was reported at 47.800 % in 2015. This records a decrease from the previous number of 49.000 % for 2008. Ivory Coast CI: Income Share Held by Highest 20% data is updated yearly, averaging 47.600 % from Dec 1985 (Median) to 2015, with 10 observations. The data reached an all-time high of 51.300 % in 1985 and a record low of 43.900 % in 1988. Ivory Coast CI: Income Share Held by Highest 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ivory Coast – Table CI.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Ivory Coast CI: Income Share Held by Third 20% data was reported at 14.600 % in 2015. This records an increase from the previous number of 14.300 % for 2008. Ivory Coast CI: Income Share Held by Third 20% data is updated yearly, averaging 14.700 % from Dec 1985 (Median) to 2015, with 10 observations. The data reached an all-time high of 15.500 % in 1988 and a record low of 13.800 % in 1985. Ivory Coast CI: Income Share Held by Third 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ivory Coast – Table CI.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Background During the years 2019-2023, Tony’s Chocolonely commissioned five annual household surveys among farmers in all of their partner cooperatives in Ghana and Côte d’Ivoire. In both countries, a research company was tasked with conducting the face-to-face interviews. The main aim of these surveys was to assess the multidimensional poverty rates, using the global Multidimensional Poverty Index (MPI). Besides the MPI-relevant items on health, education and standard of living, questionnaires also included a range of other questions. These covered various topics such as the use of external labour, livestock ownership or the ratio of cocoa Vs. non-cocoa income. The surveys were set up as a panel. In as much as possible, enumerators returned to the same respondents each year. Out of the initial 2019 sample of farmers, 45% participated in all five rounds of the panel. Sampling Simple random samples were drawn in each coop, using cooperative membership lists as sample frames. As new cooperatives in Côte d’Ivoire joined Tony’s Open Chain in 2021, 2022 and 2023, those were added to the panel. During the first two survey rounds, sample sizes were determined in proportion to the total number of farmers in the coop: in each coop, a sample of about 10% of total coop size was drawn. As Tony’s Chocolonely data needs changed in 2021, it was decided to switch to non-proportionate sampling: during the last three rounds of the panel, about 150 farmers per coop were interviewed, irrespective of coop size. For these rounds, a weight variable per country is included in the dataset for country-level analyses. MPI and HFIAS scales The provided dataset includes the ten MPI deprivation indicators, the sum of weighted MPI deprivations, as well as a binary variable to distinguish MPI-poor from MPI-non-poor households. Furthermore, the dataset includes a variable to categorize households as ‘food secure’, ‘mildly food insecure’, ‘moderately food insecure’ or ‘severely food insecure’, based on the nine items of the Household Food Insecurity Access Scale (HFIAS). Data anonymization All data have been anonymized by removing personally identifiable information, such as names, phone numbers, GPS data, detailed household composition and responses to open questions. Each respondent has been assigned a unique ID to be identifiable across rounds (famer_hash). To further increase confidentiality, coop names have been replaced by pseudonyms. Across time consistency of farmer codes Users of the dataset should note that some farmer codes may have different demographic information associated with them across different years. In most cases, this will mean that a different household member has been interviewed by the enumerator. In some cases it may be that the farmer code has been reassigned to a different household (likely due to farmers leaving the cooperative). This should not affect the representativeness of the sample per year but will introduce some noise when running time series analyses. Using the data Tony’s Open Chain encourage open use of this data and are excited to learn from how others use and analyse the data – if you carry out an analysis of this data, please do share it with us by contacting emmavandam@tonyschocolonely.com.

Ivory Coast CI: Income Share Held by Second 20% data was reported at 10.200 % in 2015. This records an increase from the previous number of 9.800 % for 2008. Ivory Coast CI: Income Share Held by Second 20% data is updated yearly, averaging 10.500 % from Dec 1985 (Median) to 2015, with 10 observations. The data reached an all-time high of 11.300 % in 1988 and a record low of 9.300 % in 1985. Ivory Coast CI: Income Share Held by Second 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ivory Coast – Table CI.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Ivory Coast CI: Income Share Held by Lowest 10% data was reported at 2.100 % in 2015. This records an increase from the previous number of 1.800 % for 2008. Ivory Coast CI: Income Share Held by Lowest 10% data is updated yearly, averaging 2.400 % from Dec 1985 (Median) to 2015, with 10 observations. The data reached an all-time high of 2.800 % in 1986 and a record low of 1.800 % in 2008. Ivory Coast CI: Income Share Held by Lowest 10% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ivory Coast – Table CI.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Ivory Coast Poverty Headcount Ratio at Societal Poverty Lines: % of Population data was reported at 31.900 % in 2021. This records a decrease from the previous number of 34.300 % for 2018. Ivory Coast Poverty Headcount Ratio at Societal Poverty Lines: % of Population data is updated yearly, averaging 37.500 % from Dec 1985 (Median) to 2021, with 12 observations. The data reached an all-time high of 44.900 % in 2008 and a record low of 28.600 % in 1986. Ivory Coast Poverty Headcount Ratio at Societal Poverty Lines: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ivory Coast – Table CI.World Bank.WDI: Social: Poverty and Inequality. The poverty headcount ratio at societal poverty line is the percentage of a population living in poverty according to the World Bank's Societal Poverty Line. The Societal Poverty Line is expressed in purchasing power adjusted 2017 U.S. dollars and defined as max($2.15, $1.15 + 0.5*Median). This means that when the national median is sufficiently low, the Societal Poverty line is equivalent to the extreme poverty line, $2.15. For countries with a sufficiently high national median, the Societal Poverty Line grows as countries’ median income grows.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Ivory Coast CI: Income Share Held by Lowest 20% data was reported at 5.700 % in 2015. This records an increase from the previous number of 5.100 % for 2008. Ivory Coast CI: Income Share Held by Lowest 20% data is updated yearly, averaging 6.200 % from Dec 1985 (Median) to 2015, with 10 observations. The data reached an all-time high of 6.900 % in 1986 and a record low of 4.900 % in 1985. Ivory Coast CI: Income Share Held by Lowest 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ivory Coast – Table CI.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Abstract

The agricultural sector in Côte d’Ivoire accounts for over 20 percent of GDP and provides employment and income to approximately half of all households. In addition, much of the manufacturing and transport sectors also depends on agriculture. The main export crops – cocoa, rubber, oil palm, cotton and cashew – play a key role in the sector’s growth and in poverty alleviation. Indeed, they are crucial income sources for smallholder farmers as well as the engines of major farming systems across the country.

In partnership with the World Bank and the French Development Agency (AFD), the Ministry of Agriculture developed the PSAC project, the effective start of which took place in May 2014. The goal of the PSAC project was to help farmers move from an extensive to an intensive and sustainable production model, based on better farming practices, the spread of high-yielding varieties, greater use of fertilizers, and appropriate mechanization. The overall development objective of the PSAC was to improve smallholder access to technologies and markets – and enhance governance of the country’s main export crop value chains.

The PSAC project had the following four components: 1) promotion of public-private partnership for sustainable cocoa development in South-West Côte d'Ivoire, 2) support to smallholder rubber and oil palm extension in South-East Côte d’Ivoire, 3) support to the cotton sector and promotion of cashew processing in Central and Northern Côte d’Ivoire and 4) project implementation and support to sector coordination. The Africa Gender Innovation Lab was engaged to conduct a gender-informed impact evaluation (IE) of targeted interventions within PSAC. After consultations with stakeholders and assessment of statistical feasibility, the aforementioned interventions within components two and three of PSAC were selected. In addition, within the first intervention, the Gender Innovation Lab, in partnership with the rubber value-chain consortium Association des Professionnels du Caoutchouc Naturel de Côte d'Ivoire (APROMAC), designed and evaluated an innovative behavioral intervention aimed at promoting inclusion of women in rubber cultivation and improving agricultural returns for farming households.

The rubber value chain impact evaluation is implemented in 10 regions of Côte d’Ivoire. 3,231 producers, corresponding to 3,054 households, were successfully surveyed at baseline in June-July 2016 before the start of the intervention. The main respondent was the household head, with specific questions on the rubber addressed to the producer in instances where the two diverged. Producers’ spouses were also asked a subset of questions.

Geographic coverage

The study gathered information from 3,231 producers in 10 regions of Côte d'Ivoire, in the South and Center areas of the country.

Analysis unit

Households, individuals, and plots of land

Sampling procedure

Raising awareness for the registration of applicants to the grant program was made from January to April 2016. The application files were verified by APROMAC administrative officers and incomplete files were eliminated. Incomplete files and the candidates who did not meet the eligibility criteria were eliminated. Of the 6,246 applicants only 4,252 (68%) were eligible. A sample of 4,005 planters was drawn from the list of eligible applicants for the program. Given the objective of having 1,000 former beneficiaries among the beneficiaries of the program in 2016, all 546 eligible former beneficiaries were chosen to be beneficiaries.

The survey was conducted with a sample of 3,231 planters, candidates to the 2016 cost-shared rubber seedling subsidy program as well as to their spouses, for those who are married. 2,117 spouses were interviewed.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The multi-topic household survey instrument covered a detailed set of questions related to basic household demographics, intra-household control of resources, agricultural plots use, and agricultural production. The land modules elicit a rich set of information on market participation, agricultural investment, and the agricultural modules allow for productivity estimates at the agricultural plot level. Each respondent was subjected to all modules, and spouses of married respondents were subjected to some parts of the modules.

Ivory Coast CI: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 41.500 % in 2015. This records a decrease from the previous number of 43.200 % for 2008. Ivory Coast CI: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 40.550 % from Dec 1985 (Median) to 2015, with 10 observations. The data reached an all-time high of 45.500 % in 1985 and a record low of 36.900 % in 1988. Ivory Coast CI: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ivory Coast – Table CI.World Bank.WDI: Poverty. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Abstract

The SWEDD is a regional project aiming to accelerate the demographic transition by addressing both supply- and demand-side constraints to family planning and reproductive and sexual health. To achieve its objective, the project targets adolescent girls and young women mainly between the ages of 8 and 24, who are vulnerable to early marriage, teenage pregnancy, and early school drop-out. The project targeted 9 countries of the Sahel and Western Africa (Benin, Burkina Faso, Cameroon, Chad, Côte d’Ivoire, Guinea, Mali, Mauritania, and Niger) and is expanding in other African countries. The SWEDD is structured into three main components: component 1 seeks to generate demand for reproductive, maternal, neonatal, child health and nutrition products and services; component 2 seeks to improve supply of these products and qualified personnel; and component 3 seeks to strengthen national capacity and policy dialogue.

The World Bank Africa Gender Innovation Lab and its partners are conducting rigorous impact evaluations of key interventions under component 1 to assess their effects on child marriage, fertility, and adolescent girls and young women’s empowerment. The interventions were a set of activities targeting adolescent girls and their communities, designed in collaboration with the government of Mauritania. These were (i) safe spaces to empower girls through the provision of life skills and SRH education; (ii) Cash transfer to cover girls’ expenses (transportation cost, food…); (iii) support to income-generating activities (IGA) with the provision of grants and entrepreneurship training and finally (iv) community sensitization by religious and village leaders. The latter two have the objective to change restrictive social norms and create an enabling environment for girls’ empowerment.

These data represent the first round of data collection (baseline) for the impact evaluation. The sample comprises 5,324 households and girls living in the regions of Assaba, Guidimagha, Hodh Charghy et Hodh Gharby.

The information gathered from the survey may aid decision makers in the formulation of economic and social policies to: - reduce fertility and child marriage by improving access to contraceptive methods and improving reproductive health knowledge. - foster women’s empowerment through enhancing their access to economic activities.

The survey can be an important source of information for planners to know how to improve the quality of people's living standards, in particular women’s living conditions. The Ministry of Social Affairs, Children and Families and the Ministry of National Education of Mauritania would benefit from the data of this survey, together with other public organizations working on girls and women empowerment and reproductive health. District Authorities, Research Institutions, Non-Governmental Organizations and the general public will also benefit from the survey data.

Geographic coverage

Four regions of Mauritania : Assaba, Guidimagha, Hodh Charghy et Hodh Gharby.

Analysis unit

Households, individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The study was conducted in 74 localities and 55 secondary schools in the regions of de Assaba, Guidimagha, Hodh Charghy et Hodh Gharby. The sampling procedure involves several steps to ensure comprehensive coverage of the target populations. For the Ministry of National Education (MEN), 55 eligible secondary schools located in the chief towns of communes have been identified. The census is conducted in these chief towns and surrounding localities where students reside. In localities with fewer than 400 households, the entire locality is surveyed. In larger localities, the survey is conducted in the enumeration zones containing the schools and one or two adjacent zones. For the Ministry of Social Affairs, Childhood and Family (MASEF), the program targets chief towns of communes not part of the "Tekavoul" program and excludes chief towns of Mougataa and Wilaya. In chief towns with fewer than 400 households, the entire town is surveyed, while in larger towns, the two enumeration zones with the highest number of women are surveyed. The MASEF safe spaces target women aged 15 to 29 who are out of school or never attended school, from households with the lowest socio-economic scores based on durable goods, access to basic infrastructure, and housing characteristics. Similarly, the MEN safe spaces target school-going girls from households with the lowest scores. The scholarship program involves individual randomization of girls sampled for MEN safe spaces, forming two groups: one receiving scholarships (1084 girls) and the other not receiving scholarships (1080 girls). This systematic approach ensures a thorough evaluation of the project's impact across different regions and target populations.

The objective of the baseline survey was to build a comprehensive dataset, which would serve as a reference point for the entire sample, before treatment and control assignment and program implementation.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The data consists of responses from households to questions pertaining to: 1. List of household members 2. Education and employment of household members 3. Characteristics of housing and durable goods 4. Chocs and food security 5. Household head's aspirations for their children 6. Attitudes on women's empowerment and gender equality

The questionnaire administrated to girls contains the following sections: 1. Education 2. Marriage and children 3. Aspirations 4. Reproductive health and family planning 5. Psycho-social 6. Women's empowerment 7. Gender-based violence 8. Income-generating activities 9. Savings and credits 10. Personal relationships and social networks 11. Migration

The household questionnaire was administered to the head of the household or to an authorized person capable of answering questions about all individuals in the household. The adolescent questionnaire was administered to an eligible pre-selected girl within the household. Considering the modules of the adolescent questionnaire, it was only administered by female enumerators. The questionnaires were written in French and programmed on tablets in French using the CAPI program.

Ivory Coast CI: Poverty Gap at $6.85 a Day: 2017 PPP: % data was reported at 34.300 % in 2021. This records a decrease from the previous number of 34.500 % for 2018. Ivory Coast CI: Poverty Gap at $6.85 a Day: 2017 PPP: % data is updated yearly, averaging 41.050 % from Dec 1985 (Median) to 2021, with 12 observations. The data reached an all-time high of 51.000 % in 2015 and a record low of 23.400 % in 1985. Ivory Coast CI: Poverty Gap at $6.85 a Day: 2017 PPP: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ivory Coast – Table CI.World Bank.WDI: Social: Poverty and Inequality. Poverty gap at $6.85 a day (2017 PPP) is the mean shortfall in income or consumption from the poverty line $6.85 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Abstract

The published data was collected in urban Côte d’Ivoire, specifically in Abidjan, the country’s capital, and Grand-Bassam, the historic capital around 33 km from Abidjan. These surveys were conducted as part of the impact evaluation of the PRO-Jeunes (Pro-Youth) program, a six-year program (2017-2022) implemented by the International Rescue Committee (IRC). The program provides flexible (self-) employment support services to youth aged 15 to 30 years in both rural and urban Côte d’Ivoire. In addition, to be eligible for the PRO-Jeunes program, applicants must be unschooled or have dropped out of school and show proof of identity. The program was designed to accommodate a wide range of beneficiaries’ needs and knowledge levels. While several cohorts were eligible for the program, we currently focus on the first two cohorts.

Innovations for Poverty Action (IPA), a non-profit research and policy organization, was in charge of the implementation of the survey. Enumerators used tablets containing programmed questionnaires on the SurveyCTO platform to collect data.

The publication contains two datasets:

baseline1_noPII: Data collected as part of the baseline survey targeting the first cohort of the PRO-Jeunes program. 1865 individuals participated in a face-to-face interview between February and July 2019.

baseline2_noPII: Data collected as part of the baseline survey targeting the second cohort of the PRO-Jeunes program. 1208 individuals were interviewed through a phone survey between May and September 2020.

KEY VARIABLES

This survey was designed to capture indicators of the following topics:
- Income-Generating activities - Earnings
- Professional projects - Entrepreneurship
- Productive equipment - Savings
- Credit - Expenditures - Aspirations - Socioemotional skills - Reproductive health
- Domestic violence
- Gender attitudes - Network

Geographic coverage

The project targeted geographically two urban areas of Côte d’Ivoire, Abidjan and Grand-Bassam. The study sample consists of 3073 youth from Abidjan (85%) and Grand-Bassam (15%)

Analysis unit

The unit of analysis is the questionnaire respondent. In other words, those eligible for Pro-Jeunes training, who attended a registration meeting and were sampled for inclusion in the study.
UNIQUE ID VARIABLE: Baseline 1 : id Baseline 2 : id1_uid

Kind of data

Sample survey data [ssd]

Sampling procedure

General Procedure : To be eligible for the PRO-Jeunes program, applicants must be out-of-school or have dropped out of school, and show proof of identity. To attract participants, the PRO-Jeunes program has been advertised in a variety of ways. Recruitment takes place in three stages. First, young people interested in the program register and express their interest. Secondly, the program team reviews all applications and calls eligible candidates to provide further information about the program and confirm their interest in taking part. Thirdly, candidates who have completed all the previous steps are invited to a registration meeting. Participants at the registration meeting who have enrolled in a training course constitute the list from which we draw our sample. From this list, we randomly sample, stratifying by gender, living area and choice of training.

Stratification variables : Baseline 1: gend_r, zone, id_choix, Baseline 2: gendc, zone, id_choix

Specifics for each Baseline :

Baseline 1: The aim was to conduct individual interviews with out-of-school young people aged between fifteen (15) and twenty-four (24) across the ten communes of the Abidjan district and the commune of Grand Bassam. Despite the difficulties encountered in the field, we interviewed 1865 young people out of the 2200 planned, i.e. a completion rate of 84.77%. However, we also used the reserve list and the complementary list, made up of 255 and 181 respectively, to carry out the various replacements throughout the collection period.

Baseline 2: 1125 interviews were expected from each of the A, B and C questionnaires. In view of the refusals, the researchers decided that the refusals should be replaced. Thus, these replacements were to carry out interviews A, B and C in succession. We received two replacement lists. The initial sample contained 1121 people, the first replacement list 227 and a second list of 184. The difficulty of the replacements lay in respecting the strata of each respondent. In other words, for each person to be replaced, we had to find an individual of the same sex, living in the same area and having chosen the same training. It has to be said that the strata were not of the same size either. The number of replacements to be made was often greater than the number of respondents remaining and available. The collection team explained the situation to the researchers, who gave them permission to make replacements outside the strata, while minimizing the gaps. This enabled us to achieve a sample size of 1,125 for surveys A, B and C, with small discrepancies in some strata.

For more information, please refer to the Survey Design and Methodology - Socioeconomic Survey of Refugees in Ethiopia (SESRE) available under Downloads.

Sampling deviation

Baseline 1: None Baseline 2: For each person to be replaced, the initial procedure was for the data collection team to find an individual of the same sex, living in the same area and having chosen the same training. The number of replacements to be made was often greater than the number of respondents remaining and available. The collection team explained the situation to the researchers, who gave them permission to make replacements outside the strata, while minimizing the gaps For more information, please refer to the data collection report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Please refer to the questionnaires uploaded for each baseline. Survey Modules
- Identification Module (ID) - Survey Situation Module (SE) - Family Context Module (CF) - Household Goods Module (BM) - Education Module (ED) - Employment Module (EM) - Activities Module (AR) - Activity Characteristics Module (AR_S) - Off-Farm Income-Generating Activities Module (AR_NA) - Agricultural Income-Generating Activities Module (AR_A) - Entrepreneurship Module (EN) - Productive Equipment Module (EP) - Professional Projects Module (PP) - Savings Module (EC) - Credit Module (CR) - Expenditure Module (DE) - Cognitive Skills Module (CC) - Gender Attitudes Module (AG) - Aspirations Module (AS) - Situational Judgment Module - Module SES Intra - Module SES Inter - Module HHI_Intra - Module Preferences (PR) - Reproductive Health Module (SR) - Domestic Violence Module (VD) - Network Module (RE) - Capture Module - End Module

Response rate

Baseline 1 : 84.77 Baseline 2: 100%

Abstract

The Sahel Women Empowerment and Demographic Dividend (P150080) project is a regional project aiming to accelerate the demographic transition by addressing both supply- and demand-side constraints to family planning and reproductive and sexual health. To achieve its objective, the project targets adolescent girls and young women mainly between the ages of 8 and 24, who are vulnerable to early marriage, teenage pregnancy, and early school drop-out. The project targeted 9 countries of the Sahel and Western Africa (Benin, Burkina Faso, Cameroon, Chad, Côte d’Ivoire, Guinea, Mali, Mauritania, and Niger) and is expanding in other African countries. The SWEDD is structured into three main components: component 1 seeks to generate demand for reproductive, maternal, neonatal, child health and nutrition products and services; component 2 seeks to improve supply of these products and qualified personnel; and component 3 seeks to strengthen national capacity and policy dialogue.

The World Bank Africa Gender Innovation Lab and its partners are conducting rigorous impact evaluations of key interventions under component 1 to assess their effects on child marriage, fertility, and adolescent girls and young women’s empowerment. The interventions were a set of activities targeting adolescent girls and their communities, designed in collaboration with the government of Côte d’Ivoire. These were (i) safe spaces to empower girls through the provision of life skills and SRH education; (ii) support to income-generating activities (IGA) with the provision of grants and entrepreneurship training; (iii) husbands’ and future husbands’ clubs, providing boys of the community with life skills and SRH education; and finally (iv) community sensitization by religious and village leaders. The latter two have the objective to change restrictive social norms and create an enabling environment for girls’ empowerment.

These data represent the first round of data collection (baseline) for the impact evaluation.

Geographic coverage

Mali, Regions of Kayes, Ségou and Sikasso

Analysis unit

Households, individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The baseline sample comprises 8776 households and 7463 girls living in the regions of Kayes, Sikasso and Ségou in Mali. To define the sample, we partnered with INSTAT Mali. At first, INSTAT conducted a census of the population living in the areas around the 49 schools selected by the education focal point that will all benefit from the SWEDD program. Therefore, census activities were concentrated in 287 villages located within a radius of 10/15km around these schools. Eventually, 10 villages had to be dropped due to security reasons. Keeping with the eligibility criteria of surveying villages where there were at least 10 households with a girl aged between 12 and 24 years old, 270 villages were eventually sampled. Households were surveyed before randomization into groups assigned to receive the SWEDD program.

The objective of the baseline survey was to build a comprehensive dataset, which would serve as a reference point for the entire sample, before treatment and control assignment and program implementation.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire administrated to girls contains the following sections: 1. Education 2. Marriage and children 3. Aspirations 4. Health and family planning 5. Knowledge of HIV/AIDS 6. Women's empowerment 7. Gender-based violence 8. Income-generating activities 9. Savings and credit 10. Personal relationships and social networks 11. Committee members and community participation

The household questionnaire was administered to the head of the household or to an authorized person capable of answering questions about all individuals in the household. The adolescent questionnaire was administered to an eligible pre-selected girl within the household. Considering the modules of the adolescent questionnaire, it was only administered by female enumerators. The questionnaires were written in French, translated into Bambara, and programmed on tablets in French using the CAPI program.

Ivory Coast CI: Poverty Gap at $5.50 a Day: 2011 PPP: % data was reported at 43.600 % in 2015. This records a decrease from the previous number of 43.800 % for 2008. Ivory Coast CI: Poverty Gap at $5.50 a Day: 2011 PPP: % data is updated yearly, averaging 38.150 % from Dec 1985 (Median) to 2015, with 10 observations. The data reached an all-time high of 43.800 % in 2008 and a record low of 17.300 % in 1986. Ivory Coast CI: Poverty Gap at $5.50 a Day: 2011 PPP: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ivory Coast – Table CI.World Bank: Poverty. Poverty gap at $5.50 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $5.50 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.