In 2023, around **** million people in Ghana lived in extreme poverty, the majority in rural areas. The count of people living on less than **** U.S. dollars a day in rural regions reached around *** million, while ******* extremely poor people were located in urban areas. Overall, within the period examined, the poverty incidence remained above *********** in rural communities and between *** thousand and *** thousand in urban areas.
As of 2023, nearly ************* people in Ghana lived in extreme poverty, with the poverty threshold at **** U.S. dollars a day. This stood as a decrease from the previous year, when over ************* people lived in the said state of poverty. The headcount was expected to keep the declining trend by 2025, when around *********** Ghanaians might live on a maximum of **** U.S. dollars per day.
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Ghana GH: Multidimensional Poverty Intensity (average share of deprivations experienced by the poor) data was reported at 51.800 % in 2018. This records an increase from the previous number of 51.700 % for 2016. Ghana GH: Multidimensional Poverty Intensity (average share of deprivations experienced by the poor) data is updated yearly, averaging 51.750 % from Dec 2010 (Median) to 2018, with 4 observations. The data reached an all-time high of 54.200 % in 2011 and a record low of 41.800 % in 2010. Ghana GH: Multidimensional Poverty Intensity (average share of deprivations experienced by the poor) data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ghana – Table GH.World Bank.WDI: Social: Poverty and Inequality. ;Government statistical agencies. Data for EU countires are from the EUROSTAT;;
The Parental Nudges Project Impact Evaluation was approved by the Strategic Impact Evaluation Fund (SIEF) of the World Bank on October 2020 in the Great Accra Region of Ghana. The official project name is called " Nudges To Improve Learning And Gender Parity: Supporting Parent Engagement And Ghana’s Educational Response To Covid-19 Using Mobile Phones”, known as “Parental Nudges Project (PNP)”. The project seeks to address the inequitable access to education and inequalities by child gender and households background during the Covid-19 pandemic through a household-level intervention designed to improve school-aged children’s outcomes by engaging parents in their children’s learning. Impacts are examined on child outcomes, including school enrollment and attendance, and learning outcomes. In addition, impacts are examined on mediating mechanisms including parental beliefs about returns to education, educational expectations and aspirations for children, engagement in education, and the rate of returning to school. Finally, with four variations of the treatment arm, impact variation is examined by duration of message receipt (12 vs 24 weeks), and by a focus on general education versus gender parity.
16 districts/municipalities across the 5 regions (Northern, North East, Savannah, Upper East, and Upper West) in the northern part of Ghana.
Primary caregivers and school-age children aged 5 to 17 years.
Five regions in the northern part of Ghana. PNP targets (a) households - parents or guardians and (b) their respective school-age children between 6 and 17 years in primary school [including kindergarten] and/or junior high school in the Northern, Savannah, North East, Upper East, and Upper West regions of Ghana. We sampled 2,500 households or primary caregivers and 5,000 (2 each) school-age children from each household.
Sample survey data [ssd]
Baseline: The sample is drawn from two previously completed studies. First, an impact evaluation of the Communications for Development (C4D) study (2012-2016), launched by the Ghana Health Service with funding from UNICEF in 12 districts of the three poorest regions of Ghana. The sample included mothers with a child aged 0-5 years recruited in 2012. The C4D program relied on voice messages directly delivered to female respondents through their cell phones. The C4D sample has high rates of mobile phone ownership (83%). Second, even in a very negative scenario in which 52% of households from the C4D sample have changed phone numbers, we relied on a subsample of the Graduating the Ultra Poor (GUP) study from the same regions to obtain our desired sample size.
The samples from the C4D and GUP projects formed the sampling frame, from which we determined the eligibility of households and then sampled from those eligible for the PNP. An initial desktop screening was conducted to screen out households without phone numbers. Households with phone numbers were then contacted through a subject enrollment call to determine their eligibility, seek their consent to participate in the study and their consent to receive the text messages.
The 4500 eligible households in the two datasets were screened to determine their eligibility for the study, with the goal of selecting 2,500 households / primary caregivers (and 2 children per household) into either one of the experimental groups.
First, eligible 4500 households were identified in the dataset and randomization was conducted before households were recruited.
Second, each of the 2500 eligible households was called to confirm eligibility and enroll them in the study. The eligibility criteria were as follows: 1. A household with at least one member owning a working mobile phone. 2. A household with an adult aged 18 years and above. This age criterion satisfies the age requirement as provided in the 2014 The Unsolicited Electronic Communications Code of Conduct by the National Communications Authority of Ghana. It also forms the basis for recruiting the primary caregiver, defined as the person who is primarily responsible for the care and education of the school-aged children in the household and could best talk about their experiences in school and at home. 3. A household that is willing to participate in the study and/or to receive text messages. 4. A household with more than one member including at least 1 school-aged child.
Finally, 2 school-aged children were randomly sampled from each eligible household (5,000 total). Additionally, using a reserve list of replacement households, we replaced 128 households that had dropped out of the study after the Subject Enrollment and Caregiver Survey.
Midline and endline: The sampling frame for the PNP consisted of two previously completed IPA studies. First, an impact evaluation of the Communications for Development (C4D) study (2012-2016), launched by the Ghana Health Service with funding from UNICEF in 12 districts of the three poorest regions of Ghana. The sample included mothers with a child aged 0-5 years recruited in 2012. The C4D program relied on voice messages directly delivered to female respondents through their cell phones. The C4D sample has high rates of mobile phone ownership (83%). Second, even in a very negative scenario in which 52% of households from the C4D sample have changed phone numbers, we relied on a subsample of the Graduating the Ultra Poor (GUP) study from the same regions to obtain our desired sample size.
A target sample of 2,500 primary caregivers/households was set. We assumed an average of 2 eligible school-going children (aged 5 to 17 years) per household and thus, planned to recruit approximately 5,000 school-going children (2 per household) from the 2,500 households. Our goal was to randomly sample one = younger school-aged child (5-9 years) and one older school-aged child (10-17 years). Taking into account an estimated 10% non-response rate and pilot sample, we targeted a sample of 2,750 households or primary caregivers. We followed the following steps in order to select the primary caregivers and children for the PNP. The steps involved screening study participants, assigning them to the intervention and comparison groups, and enrolling in the study.
Step 1. Desktop screening and random assignment of mobile phone numbers or households. We conducted desktop screening using the existing samples from the C4D and GUP to determine whether at least one household member within each household had a mobile phone number. Households without at least one household member with a mobile phone number were screened out. The desk-top screening resulted in 4,500 households with mobile phone numbers. Next, we randomly assigned the 4,500 households to the intervention and comparison groups. The random assignment resulted in an almost equal distribution of households across the intervention and comparison groups.
Step 2. Screening and enrollment of eligible households or primary caregivers. We implemented a phone-based subject enrollment call to (a) determine the eligibility of the households or primary caregivers for the study, (b) seek their consent and that of their children to be enlisted into the study, (c) inform them about the project, the intervention, and implementation modalities, (d) seek their consent to participate in the intervention, and (e) enroll 2,668 eligible primary caregivers into the study. The additional 168 households were selected to (a) serve as reserve households for replacement (n = 128) and (b) be used for the intervention piloting (n = 40).
We enrolled households or primary caregivers based on the following eligibility criteria: a. A household with an adult aged 18 years and above. This age criterion satisfies the age requirement as provided in 2014 The Unsolicited Electronic Communications Code of Conduct by the National Communications Authority of Ghana. It also forms the basis for recruiting the primary caregiver, defined as the person who is primarily responsible for the care and education of the school-aged children in the household and could best talk about their experiences in school and at home. b. A household that is willing to participate in the study and/or to receive text messages. c. A household with more than one member including at least 1 school-aged child. A child roster was created for school-going children aged 5 to 17 years within the household.
Step 3. Selection of school-going children. Once the primary caregiver had been identified, the SurveyCTO in-built randomization protocol was triggered to randomly select at most 2 school-going children [and 2 replacements] from each household. School-going children who meet the eligibility criteria, namely, (a) aged 6-17 years, (b) attending school, and (c) meeting the household residency requirement were randomly selected for the study.
The SurveyCTO in-build randomization code was designed to: a. Generate two age groups or strata of eligible school-aged children based on their age: (a) Stratum A comprised school-going children aged 6 to 9 years, (b) Stratum B consisted of school-going children aged 10 to 17 years. If there was no child aged 5-17, the selection was based on children aged 5, if available. b. Randomly sample 3 eligible school-going children from each stratum and rank them from 1 to 3. The child with rank 1 in either stratum became the selected child. Those with ranks 2 and 3 in either stratum became the replacement children. If Stratum A (Stratum B) has no eligible child, the children in Stratum B (Stratum A) with ranks 1 and 2 became the selected children. Rank 3 was the replacement child. If either stratum had less than 3 eligible children, the child
As of 2024, some 6.9 million people in Ghana lived in extreme poverty, with the poverty threshold at 2.15 U.S. dollars per day. This stood as an increase from the previous year when roughly 6.8 million people lived in the said state of poverty. In 2026, around 6.7 million Ghanaians are expected to live on a maximum of 2.15 U.S. dollars daily.
Poverty in the country is segregated
Indeed, poverty figures do not considerably vary when considering men and women apart. In 2024, around 3.5 million men lived in extreme poverty in Ghana, while the count reached roughly 3.3 million for women. On the other hand, in distinguishing the state of extreme poverty among rural and urban dwellers, the difference is striking, even when based on the previously set poverty line of 1.90 U.S. dollars per day. Overall, 1.1 percent of the world's population in extreme poverty lived in Ghana as of 2024.
Ghana's Private Wealth Position in Africa
Ghana is one of the African countries with the highest private wealth concentration, ranking 6th after Kenya as of 2021. That year, the country's total private wealth amounted to 59 billion U.S. dollars, corresponding to around 1,900 U.S. dollars per capita. Between 2011 and 2021, the total wealth held by individuals in Ghana increased, representing a higher growth in comparison to other African countries save five. Overall, the nation ranks 9th in Africa in terms of countries with high net-worth individuals.
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Ghana GH: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 42.400 % in 2012. This records a decrease from the previous number of 42.800 % for 2005. Ghana GH: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 39.250 % from Dec 1987 (Median) to 2012, with 6 observations. The data reached an all-time high of 42.800 % in 2005 and a record low of 35.300 % in 1987. Ghana GH: 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 Ghana – Table GH.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.
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Ghana GH: Coverage: Social Insurance Programs: Poorest Quintile: % of Population data was reported at 60.580 % in 2012. This records an increase from the previous number of 2.342 % for 2005. Ghana GH: Coverage: Social Insurance Programs: Poorest Quintile: % of Population data is updated yearly, averaging 31.461 % from Dec 2005 (Median) to 2012, with 2 observations. The data reached an all-time high of 60.580 % in 2012 and a record low of 2.342 % in 2005. Ghana GH: Coverage: Social Insurance Programs: Poorest Quintile: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ghana – Table GH.World Bank.WDI: Social Protection. Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;
As part of the US government’s Feed the Future initiative that aims to address global hunger and food security issues in sub-Saharan Africa, the US Agency for International Development is supporting three multi-stakeholder agricultural research projects under Africa Research In Sustainable Intensification for the Next Generation (Africa RISING - AR) program. The overall aim of the program is to transform agricultural systems through sustainable intensification projects in Ghana, Ethiopia, Tanzania, Malawi, Mali, and (potentially) Zambia. In West Africa, IITA works with multi-disciplinary R4D partners in selected communities located in Northern Ghana and Southern Mali. More particularly, in Northern Ghana three regions were chosen for the study: the Northern, Upper-East and Upper-West regions. These areas cover both maize-based and rice-vegetables-based systems and therefore allow to address the production constraints characterizing both realities7. As IFPRI (2012) highlights, the northern regions of Ghana are characterized by small land holdings and low input - low output farming systems, which adversely impact food security. In particular, they are subject to a seasonal cycle of food insecurity of three to seven months for cereals (i.e., maize, millet and sorghum) and four to seven months for legumes (i.e., groundnuts, cowpeas, and soybeans). These crops in the savannahs are often produced in a continuous monoculture, steadily depleting soil natural resources and causing the yields per unit area to fall to very low levels. The poverty profile of Ghana identifies the three northern regions as the poorest and most hunger-stricken areas in the country. Gender inequalities are also apparent in these regions, since women have limited access to resources and therefore limited capacity to generate income on their own.
Abstract copyright UK Data Service and data collection copyright owner. The Millennium Villages Project (MVP) is a 'proof of concept' project to support African rural communities in meeting the Millennium Development Goals (MDGs). Scientists in agriculture, nutrition and health, economics, energy, water, environment and information technology, working together with communities, local partners, and governments empower and backstop villages to accelerate progress in achieving the MDGs. The concept to prove is that science-based interventions, appropriate local institutions and community participation and empowerment can be combined to achieve the MDGs, within the cost estimates derived by The UN Millennium Project. The UK Department for International Development (DFID) has provided a grant of £11.5 million to implement a new Millennium Village in northern Ghana (distributed via its DFID-Ghana centre, based in Accra). The MV ran for several years from 2012 to 2016 with interventions targeting a cluster of communities with a total population of around 26,000. In the survey rounds of 2012, 2014 and 2016 the full MVP package of questionnaires was administered by the Earth Institute to track progress on the MDGs. The additional survey rounds of 2013 and 2015 administered a restricted version of the MVP household questionnaire focusing on tracking progress in poverty reduction. Users should note that the current study includes data from Years 0, 1, 2, 3 and 4 of the study, conducted in 2012, 2013, 2014, 2015 and 2016 respectively.The site is located in Savannah Accelerated Development Authority (SADA) Region in Northern Ghana. It encompasses 34 communities located in three Area Councils, in the poorest sections of two District Assemblies. While MVPs across Africa have established their own monitoring and evaluation systems, designed by the Earth Institute at Columbia University, DFID has requested that the new MVP in northern Ghana be accompanied by an independent evaluation to build on, expand and validate the MVP's own monitoring and evaluation systems. The independent evaluation team of the northern Ghana MVP will use a difference-in-difference (DD) approach, by comparing the change in outcomes in the MVP areas before implementation to post-implementation, with changes in the same outcomes for an explicit control group. DD allows the evaluation to isolate the MVP impact on the MDGs from effects of other variables changing over time.Further information may be found on the Institute for Development Studies Millennium Villages in Northern Ghana Impact Evaluation webpage. Main Topics: The 'SADA-North Ghana Household Survey' is a survey instrument used to collect household-level data from communities in the treatment (MVP), control-near and control-far areas. The instrument covers modules on: the household roster; in-migration; out-migration; education; employment; malaria prevention; food, water and energy security; water use; sanitation; energy use; shocks to household welfare; common property resources, household construction; household assets; consumption and expenditure; savings; other sources of income; credit; land ownership and use; agricultural activities, livestock; animal-based products; social networks; project participation; crime; and, expectations. Multi-stage stratified random sample Face-to-face interview
Since 1987, the Ghana Statistical Service (GSS) has been conducting the Ghana Living Standards Survey (GLSS) with the aim of measuring the living conditions and well-being of the population. The GLSS has been useful to policy makers and other stakeholders as it provides timely and reliable information about trends in poverty and helps identify priority areas for policy interventions that aim at improving the lives of the population. It has, over the years, served as one of the primary tools used in monitoring progress on poverty reduction strategies in the country. Monitoring poverty is an essential part of the struggle to end it.
The survey provides the required data at the regional and urban/rural levels for examining poverty and associated indicators for households and the population. The data also allow for decomposition of poverty changes between different groups: urban/rural, locality, region, and socioeconomic status.
Since the fifth round of the Ghana Living Standards Survey (GLSS5) in 2005, the Ghanaian economy benefited from the production of crude oil in commercial quantities and strong economic growth in 2011, leading to the achievement of lower-middle-income status for the country. Economic growth decreased thereafter to a low of 3.7 percent in 2016 but increased in 2017. However, it remains to be seen whether this growth has benefitted all sections of society, including the poorest. Several social intervention programs, including the Livelihood Empowerment Against Poverty (LEAP), Capitation Grant and School Feeding Programme, and now the Free Senior High School Programme started in 2017, have been implemented with the aim of alleviating poverty among the vulnerable population.
Poverty has many dimensions and is characterized by low income, malnutrition, ill-health, illiteracy, and insecurity, among others. The impact of the different factors could combine to keep households, and sometimes whole communities, in abject poverty. To address these, reliable information is required to develop and implement policies that would have an impact on the lives of the poor and vulnerable.
National Coverage
Households
Sample survey data [ssd]
The sampling employed a two-stage stratified sampling design. One thousand (1,000) enumeration areas (EAs) were selected to form the Primary Sampling Units (PSUs). The PSUs were allocated into the 10 administrative regions using probability proportional to population size (PPS). The list of EAs from which the samples were drawn was based on the 2010 Population and Housing Census. The EAs were further divided into urban and rural localities of residence. A complete listing of households in the selected PSUs was undertaken to form the Secondary Sampling Units (SSUs). At the second stage, 15 households from each PSU were systematically selected. The total sample size came to 15,000 households nationwide. The sampling is discussed in detail in the appendix of the reports attached as documentation/external resources.
The application system for the collection of data was developed in CSPro software. All electronic data files for the GLSS7 were transferred remotely from the field (data collection locations) to GSS Head Office in Accra. Various levels of data protection measures were employed to ensure confidentiality of respondents' identification details and security of the data. Data editing, cleaning, coding and processing all started soon after data collected from the field were transferred to Head Office. The editing and cleaning included structure and consistency checks to ensure completeness of work in the field. It also included identification of outliers. Any inconsistencies identified in completed questionnaire from a particular EA were documented and reported to the team responsible to correct before they left the EA. Secondary editing, which required resolution of computer-identified inconsistencies was also undertaken. Even though most sections of the questionnaire were pre-coded some sections required coding in the office. This involved the assignment of numbers (codes) to the occupation and industry in which eligible household members worked using the detailed descriptions provided by the interviewer. Cleaning and aggregation of data were on-going as data were transferred from the field. The data processing including cleaning and aggregation started in October, 2017 and was completed in February, 2018.
The response rate was 93.3%.
In West Africa, IITA works with multi-disciplinary Results for Development (R4D) partners in selected communities located in Northern Ghana and Southern Mali. More particularly, in Northern Ghana three regions were chosen for the study: the Northern, Upper-East and Upper-West regions. These areas cover both maize-based and rice/vegetables-based systems and therefore allow to address the production constraints characterizing both realities. As IFPRI (2012) highlights, the northern regions of Ghana are characterized by small land holdings and low input / low output farming systems, which adversely impact food security. In particular, they are subject to a seasonal cycle of food insecurity of three to seven months for cereals (i.e., maize, millet and sorghum) and four to seven months for legumes (i.e., groundnuts, cowpeas, and soybeans). These crops in the savannahs are often produced in a continuous monoculture, steadily depleting the soil's natural resources and causing the yields per unit area to fall to very low levels. The poverty profile of Ghana identifies the three northern regions as the poorest and most hunger-stricken areas in the country. Gender inequalities are also apparent in these regions, since women have limited access to resources and therefore limited capacity to generate income on their own.
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Ghana GH: (DC)Benefit Incidence: Social Insurance Programs to Poorest Quintile: % of Total Social Insurance Benefits data was reported at 0.678 % in 2012. This records a decrease from the previous number of 2.687 % for 2005. Ghana GH: (DC)Benefit Incidence: Social Insurance Programs to Poorest Quintile: % of Total Social Insurance Benefits data is updated yearly, averaging 1.683 % from Dec 2005 (Median) to 2012, with 2 observations. The data reached an all-time high of 2.687 % in 2005 and a record low of 0.678 % in 2012. Ghana GH: (DC)Benefit Incidence: Social Insurance Programs to Poorest Quintile: % of Total Social Insurance Benefits data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ghana – Table GH.World Bank.WDI: Social Protection. Benefit incidence of social insurance programs to poorest quintile shows the percentage of total social insurance benefits received by the poorest 20% of the population. Social insurance programs include old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;
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In 2012, Ghana Health Services (GHS), with funding from the United Nations Children’s Fund (UNICEF), launched a Communication for Development (C4D) program in twelve districts of the four poorest regions of Ghana. The main objective of this program was to encourage families to adopt and consistently practice five health behaviors which are critical for preventing under-five mortality: sleeping under an insecticide-treated mosquito net (ITN), utilization of oral rehydration solution (ORS) for the treatment of diarrhea, hand-washing with soap, exclusive breastfeeding and delivery with a skilled birth attendant. The C4D intervention package tried to achieve behavioral change through four main activities: (1) Home visits and counseling by Community-Based Agents (CBAs) affiliated with GHS; (2) Ghana Community Radio Network (GCRN) community radio broadcasts of focus group discussions and jingles; (3) Theatre dramas supported by the Center for National Culture (CNC); and (4) Video screening of recorded drama supported by the CNC. Additionally, a mobile messaging intervention (Mobile for development or M4D) was developed to complement the ongoing C4D activities. The M4D program targeted the same behaviors as the C4D program. Rather than relying on home visits, drama or radio programming, the program relied on voice messages directly delivered to female respondents through their cell phones. The main objective of this evaluation was to determine the impact of the C4D and M4D programs on the practice of the five key behaviors mentioned above. In order to allow for a rigorous evaluation of these programs, the C4D program was randomly rolled out at the community level, while the M4D program was randomized at the individual level. To capture changes in behavior, three survey rounds were conducted: A baseline survey in 2012, a midline survey in 2014 and an endline survey in 2016. Two round of qualitative data collection were also conducted to complement and explain the findings of the quantitative analysis.
The 2003 Core Welfare Indicators Questionnaire (CWIQ) Survey is a nationwide sample survey, designed to provide indicators for monitoring poverty and living standards in the country, at national, regional and district levels. It is a district-based probability sample that covered a total of 49,003 households nationwide, with 405 households drawn from each district, except for the metropolitan areas, which had samples of households as follows: Accra, 2,430; Kumasi, 11,620; and Shama-Ahanta East, 1,215; as well as the Tema Municipal Area, 810.
Key Findings were as follows:
Adult Literacy
About 50 per cent of the population aged 15 years can read and write (53.4 per cent), an increase of about 10 per cent over the rate recorded in the 1997 CWIQ Survey. Males have a higher literacy rate than females, 65.8 per cent compared to 42.3 per cent. There is a 30 percentage point gap between urban and rural literacy rates (69.6 per cent and 39.8 per cent respectively). Females are more disadvantaged in rural areas where the literacy rate is less than 30 per cent compared urban areas where the rate is more than 50 per cent. The female literacy rates are also lower than the male rates in both urban and rural areas of the country.
Youth Literacy
Among the youth, i.e., the population aged 15 - 24 years, the proportion that can read and write increased only slightly from 64.1 per cent in 1997 to 68.7 per cent in 2003. The female youth made some modest gains in their literacy levels, which increased by 10 per cent, while that of males increased by only 4 per cent over the five-year period. The literacy rate for urban youth (81.7 per cent) is considerably higher than that of the rural youth (56.4 per cent). The rural poor have however remained disadvantaged, with just a third of its females and less than half of its males being able to read and write.
Net Enrolment
Seven in 10 children aged 6 to 11 years are enrolled in primary school, for girls as for boys. The differences between the enrolment rates for girls and boys at the national level, and in the rural and urban areas are marginal. The biggest gender gap is 2.4 percentage points among the urban poor, with boys having the edge.
Substantially fewer children progress from primary to secondary level. Of the children aged 12 to 17 years, only about 4 in 10, are enrolled in secondary school, and the gender disparity in 1997 has reversed. Overall, enrolment at the secondary level declined marginally, from 40.0 percent in 1997 to 38.1 per cent in 2003. The rate however declined appreciably for males (from 43.6 to 37.9 per cent) but increased slightly for females (from 36.4 to 38.4 per cent) over the five year period. There are substantial differences between the urban and rural areas (50.5 per cent compared to 28.7 per cent), and between the poor in urban and rural areas (40.3 per cent compared to 15.2 per cent).
Access to School
A high proportion of primary school children (85.4 per cent) have a primary school within 30 minutes of their home, compared to only 43.3 per cent, for secondary schools. Access to a primary school is substantially high for all four subgroups - rural versus urban and rural poor versus urban poor. The rural poor have the lowest access rate (72.7 per cent), with 93.4 percent of the urban poor reporting access. In contrast, about 62.6 per cent of secondary level students in urban areas, but only 28.8 per cent of their counterparts in rural areas have a secondary school within 30 minutes of their home. The corresponding proportions for the
urban and rural poor are 55.1 and 12.9 per cent, respectively.
Satisfaction with Education
About two-thirds (68.0 per cent) of all primary school children report being satisfied with the school they attend while a higher proportion (75.0 per cent) of the secondary school students report being satisfied with their school. However, primary pupils and secondary students in rural areas, especially the rural poor, are less satisfied with their schools than their counterparts in the urban areas.
Access to Health Facilities.
The time required to reach a health facility could affect the chances of survival of sick people, especially in emergency situations. Yet, only 57.6 per cent of the population live within 30 minutes of a health facility. This is however a significant improvement over the 1997 average of 37.2 per cent. More than three quarters (78.5 per cent) of urban households have good access to health facilities compared to 42.3 per cent of the rural households. The urban poor have an access rate (72.7 per cent) below the average rate for all urban areas (78.5 percent); while the rural poor is more disadvantaged, relative to their counterparts - in all rural areas and the urban poor. Only 27 per cent of the rural poor live within 30 minutes of a health facility.
Adequacy of Health Services
About 18 per cent of the population reported having been sick or injured in the four-week period preceding the survey, and there has been little change in the situation since 1997 (18.6 percent). In general, only 18.4 per cent of the people consult a health practitioner. Nearly eight out of ten (78.6 per cent) persons who use health services are satisfied with the services they receive, a considerable improvement over the 1997 rate of 57 per cent. The level of satisfaction with the medical services show very little variation across groups. Equal proportions of rural and urban users of the health services are satisfied, and a slightly lower percentage of the rural than urban poor users of these facilities are satisfied.
Prenatal Care
About nine in ten women (93.4 per cent) aged 12-49 years who had live births within 12 months of the survey, received prenatal care. The urban and rural poor have lower participation in prenatal care than their counterparts. The proportion of these women who received prenatal care is 95.9 per cent for the urban poor, and 97.3 per cent for all urban areas. Similarly, the rural poor have lower participation in prenatal care than all rural areas; 86.5 per cent compared to 91.2 percent, respectively.
Births Assisted by Trained Health Professionals
About half of the children aged under five years, were delivered with the assistance of a trained health professional (doctors, nurses and midwifes) in 2003 (51.8 per cent), an increase over the proportion in 1997 (44.7 per cent). The involvement of trained professionals in birth deliveries is more than twice as high in the urban areas (83.3 per cent), than in the rural areas (34.7 per cent). The rate of professionally assisted births is extremely low among the rural poor, for whom the corresponding proportion is only 17.3 per cent compared to that for the urban poor, almost four times as high.
Child Nutritional Status
Of the three anthropometric indicators of malnutrition (stunting, wasting and underweight), stunting is the most prevalent among the children aged 0-4 years. Nearly one-third (32.4 percent) of the children under the age of five years are stunted (short for their age) compared to 15.5 per cent for wasted (underweight for age for height) and 25.8 per cent for underweight (underweight for their height for age). Stunting is higher in rural children (33.6 per cent) than in urban children (30.0 per cent), while children of the poor in both rural and urban areas are worse off relative to the national average. However, the urban rates for both wasting and
underweight are considerably higher than the rural rates, and the urban rates are higher than the national average, while the rural rates are lower. While the level of underweight barely changed over the five year period, (26.0 per cent, in 1997), the rates of stunting and wasting have worsened, and in the case of wasting, it is more than double the 1997 rate (6.5 per cent).
Availability of Employment
The proportion of the population aged 15 years and older who are unemployed averaged 5.4 percent, a slight increase over the 1997 figure (4.6 per cent). The proportion for urban areas (7.6 per cent) is about twice that of rural areas (3.5 per cent). The underemployment rate stood at 13.6 per cent, with the rural rate being 14.9 per cent, and urban, 12.1 per cent.
Meeting Food Needs
More than a tenth (12.8 per cent) of the households report having problems to meet their basic food needs. However, this problem is more prevalent among the rural poor. The proportion of rural households that have difficulty meeting their basic food needs is slightly higher (13.8 per cent) than for urban areas (11.6 per cent).
Access to Water
More than 90 per cent of households are within 30 minutes of their source of drinking water, compared to 82.1 per cent recorded in 1997. Both the rural and urban households record an access level of over 90 per cent. The rural poor have a lower access rate of 83.1 per cent, compared to 94.9 per cent for the urban poor.
Improved Water Source
The quality of drinking water is of great importance to the health of every individual. A higher percentage of households obtain their drinking water from improved water sources- pipe water in the dwelling, outdoor tap, borehole, and protected well-(74.1 per cent), compared to the 1997 figure of 65.2 per cent. Urban households record a higher percentage than rural households (87.3 per cent and 63.0 per cent, respectively), with over 20 percentage points difference.
Safe Sanitation
Safe sanitation, defined as the use of flush toilet, covered pit latrine and VIP/KVIP, is available to 55 per cent of households. Although this represents an improvement over the 1997 rate of 45.8 per cent, safe sanitation is more of an urban (80.9 per cent) than rural phenomenon (33.1 per cent). Safe sanitation facilities are even scarcer among the rural poor, with only 9.2 per cent of their households with these facilities. Moreover, the proportion of urban poor households with safe sanitation (66.9 per cent),
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Ghana GH: Benefit Incidence: Social Protection & Labour Programs (SPL) to Poorest Quintile: % of Total SPL Benefits data was reported at 0.678 % in 2012. This records a decrease from the previous number of 3.570 % for 2005. Ghana GH: Benefit Incidence: Social Protection & Labour Programs (SPL) to Poorest Quintile: % of Total SPL Benefits data is updated yearly, averaging 2.124 % from Dec 2005 (Median) to 2012, with 2 observations. The data reached an all-time high of 3.570 % in 2005 and a record low of 0.678 % in 2012. Ghana GH: Benefit Incidence: Social Protection & Labour Programs (SPL) to Poorest Quintile: % of Total SPL Benefits data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ghana – Table GH.World Bank.WDI: Social Protection. Benefit incidence of social protection and labor programs (SPL) to poorest quintile shows the percentage of total social protection and labor programs benefits received by the poorest 20% of the population. Social protection and labor programs include social insurance, social safety nets, and unemployment benefits and active labor market programs. Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;
Abstract copyright UK Data Service and data collection copyright owner. The Millennium Villages Project (MVP) is a 'proof of concept' project to support African rural communities in meeting the Millennium Development Goals (MDGs). Scientists in agriculture, nutrition and health, economics, energy, water, environment and information technology, working together with communities, local partners, and governments empower and backstop villages to accelerate progress in achieving the MDGs. The concept to prove is that science-based interventions, appropriate local institutions and community participation and empowerment can be combined to achieve the MDGs, within the cost estimates derived by The UN Millennium Project. The UK Department for International Development (DFID) has provided a grant of £11.5 million to implement a new Millennium Village in northern Ghana (distributed via its DFID-Ghana centre, based in Accra). The MV ran for several years from 2012 to 2016 with interventions targeting a cluster of communities with a total population of around 26,000. In the survey rounds of 2012, 2014 and 2016 the full MVP package of questionnaires were administered by the Earth Institute to track progress on the MDGs. The additional survey rounds of 2013 and 2015 administered a restricted version of the MVP household questionnaire focusing on tracking progress in poverty reduction. Users should note that the current study includes data from Years 0, 1, 2 and 3 of the study, conducted in 2012, 2013, 2014 and 2015 respectively. The site is located in Savannah Accelerated Development Authority (SADA) Region in Northern Ghana. It encompasses 34 communities located in three Area Councils, in the poorest sections of two District Assemblies. While MVPs across Africa have established their own monitoring and evaluation systems, designed by the Earth Institute at Columbia University, DFID has requested that the new MVP in northern Ghana be accompanied by an independent evaluation to build on, expand and validate the MVP's own monitoring and evaluation systems. The independent evaluation team of the northern Ghana MVP will use a difference-in-difference (DD) approach, by comparing the change in outcomes in the MVP areas before implementation to post-implementation, with changes in the same outcomes for an explicit control group. DD allows the evaluation to isolate the MVP impact on the MDGs from effects of other variables changing over time. Further information may be found on the Institute for Development Studies Millennium Villages in Northern Ghana Impact Evaluation webpage. For the fourth edition (September 2018), data and documentation for Year 4 (2016) were added to the study. Main Topics: The 'SADA-North Ghana Household Survey' is a survey instrument used to collect household-level data from communities in the treatment (MVP), control-near and control-far areas. The instrument covers modules on: the household roster; in-migration; out-migration; education; employment; malaria prevention; food, water and energy security; water use; sanitation; energy use; shocks to household welfare; common property resources, household construction; household assets; consumption and expenditure; savings; other sources of income; credit; land ownership and use; agricultural activities, livestock; animal-based products; social networks; project participation; crime; and, expectations. Multi-stage stratified random sample Face-to-face interview
In urban households in Ghana as of 2018, **** percent of them fell into the quintile category of the richest, and this was followed by the household population in the fourth quintile group. *** percent of the given urban population belonged to the poorest group (first quintile). Overall, percentages of household wealth index quintiles are higher in the poorest group in rural communities, contrary to urban households which are higher in the fifth quintile (richest).
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Ghana GH: Coverage: Unemployment Benefits & Active Labour Market Programs: Poorest Quintile: % of Population data was reported at 11.759 % in 2012. This records an increase from the previous number of 0.388 % for 2005. Ghana GH: Coverage: Unemployment Benefits & Active Labour Market Programs: Poorest Quintile: % of Population data is updated yearly, averaging 6.074 % from Dec 2005 (Median) to 2012, with 2 observations. The data reached an all-time high of 11.759 % in 2012 and a record low of 0.388 % in 2005. Ghana GH: Coverage: Unemployment Benefits & Active Labour Market Programs: Poorest Quintile: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ghana – Table GH.World Bank: Social Protection. Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;
In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.
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IntroductionGhana implemented a universal health coverage scheme aimed at attaining financial risk protection against catastrophic out-of-pocket health expenditures. The effort has yielded mixed benefits for the different socio-economic profiles of the population. The present study estimates the incidence of catastrophic payments among Ghanaian households.MethodsThe study analyzed the round seven dataset of the Ghana Living Standards Survey collected between 2016 and 2017. We estimated the incidence and intensity of catastrophic payments for total household consumption and non-food consumption for a range of thresholds. The analysis further weighted the measures of catastrophic payments to determine the distribution sensitivity.ResultsAs the threshold increased from 10 to 25% of total household consumption, the incidence of catastrophic payments dropped from 1.0 to 0.1%. At the 40% threshold of non-food consumption, the estimated incidence was 0.2%. For both total household consumption and non-food consumption, the concentration indices were negative at all the thresholds. The results were indicative of a higher concentration of financial catastrophe among the poorest households and significant inequalities in the incidence between the poorest and richest households.ConclusionThe study confirmed the declining trend in the general incidence of catastrophic health expenditures in Ghana. However, the incidence and risk of financial catastrophe remained disproportionately higher among the poorest households, which is instructive of gaps in financial risk protection coverage. The Ghana National Health Insurance Scheme must therefore strengthen its targeting and enrolment of this sub-population group to reduce their vulnerability to catastrophic payments.
In 2023, around **** million people in Ghana lived in extreme poverty, the majority in rural areas. The count of people living on less than **** U.S. dollars a day in rural regions reached around *** million, while ******* extremely poor people were located in urban areas. Overall, within the period examined, the poverty incidence remained above *********** in rural communities and between *** thousand and *** thousand in urban areas.