Nigeria NG: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 43.000 % in 2009. This records an increase from the previous number of 40.100 % for 2003. Nigeria NG: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 43.000 % from Dec 1985 (Median) to 2009, with 5 observations. The data reached an all-time high of 51.900 % in 1996 and a record low of 38.700 % in 1985. Nigeria NG: 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 Nigeria – Table NG.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.

According to governmental data from 2020, the Gini coefficient in Nigeria was **** points as of 2019. The Gini index gives information on the distribution of income in a country. In an ideal situation in which incomes are perfectly distributed, the coefficient is equal to zero.

The first eight countries with the biggest inequality in income distribution in the world are located in Sub-Saharan Africa, with an index over ** points.

Comparing the *** selected regions regarding the gini index , South Africa is leading the ranking (**** points) and is followed by Namibia with **** points. At the other end of the spectrum is Slovakia with **** points, indicating a difference of *** points to South Africa. The Gini coefficient here measures the degree of income inequality on a scale from * (=total equality of incomes) to *** (=total inequality).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than *** countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

39,00 (%) in 2018. Der GINI-Index misst, zu welchem Ausmaß die Einkommensverteilung oder die Konsumausgaben von Individuen oder Haushalten innerhalb einer Wirtschaft von der idealen, gleichmäßigen Verteilung abweichen. Mithilfe einer Lorenzkurve werden die kumulierten Prozentsätze des Gesamteinkommens und die kumulierte Anzahl der Personen, die Einkommen beziehen, angefangen mit dem ärmsten Individuum oder Haushalt, dargestellt. Der GINI-Index misst die Fläche zwischen der Lorenzkurve und einer hypothetischen Linie, die die perfekte Verteilung symbolisiert und wird als Prozentsatz der maximalen Fläche unter dieser Linie angegeben. Somit bedeutet ein GINI-Index von 0 eine absolut gleichmäßige Verteilung, ein Index von 100 eine absolute Ungleichheit.

Fish products are highly traded and global fish trade has been increasing very rapidly in recent decades with an estimated 45% of the world catch now traded internationally. West Africa has a huge potential for trade in intra-regional terms and vibrant markets for fish and fish products in Nigeria, Ghana and the Ivory Coast being the three major importers of fish products in the region. Intra-regional fish trade is important in improving food and nutritional security; and poverty eradication in Africa. However, trade has so far not served as an effective tool for the achievement of rapid and sustainable economic growth and development for many of the countries of the continent because, there is paucity of information on market structure and value of intra-regional fish trade. This information is needed to ensure the proper integration of intraregional fish trade into the nation-state policy agenda. This study therefore investigated the marketing structure, profitability and trade flow along the borders of Nigeria. In this study, Nigeria was divided into four major areas according to the countries at her borders viz: Nigeria-Cameroon-Chad, Nigeria-Niger, and Nigeria-Benin border and the Lake Kainjiinland fisheries with multi-stage experimental design adopted. Sixteen States including Akwa Ibom, Cross River, Benue, Taraba, Adamawa, Borno, Sokoto, Katsina, Jigawa, Yobe, Kebbi, Kwara, Oyo, Ogun, Lagos and Niger States along the borders were sampled. One hundred and eight (108) Local Governments Areas in the States were sampled based on the prevalence of fishing activities in these areas. Structured questionnaires were administered to a total of 814 producers, 814 processors and 814 marketers randomly selected based on their active involvement in fish marketing. Information on socio-economic characteristics, quantity and values of fish products marketed, trade flow and market structure were collected. Data collected were subjected to descriptive statistics, budgetary analysis, Gini coefficient, linear regression and ANOVA at α0.05. The results showed that fish production was dominated by males along the three borders with the highest percentage recorded in Sokoto and Katsina (100.0%), while more females were involved in processing and marketing in four States (Cross River, Akwa Ibom, Taraba and Benue States) along the Nigeria-Cameroon-Chad border with the highest percentage in Cross River (90.0% and 90.0%). About 1.69% of the fishermen along Nigeria-Cameroon border import fresh while 0.67% of the fish marketers import smoked and dried fish into Nigerian fish market. Marketers and processors of 1.51% were involved in cross-border importation of smoked fish into Nigeria through Nigeria-Benin border while 0.81% of the sampled marketers engaged in importation of fresh and dried fish products. 0.40% of the marketers and processors along Nigeria-Niger border and Lake Kainji-inland fisheries were involved in cross-border trade of dried and fried fish products. About 1.19% of the fish marketers export fried fish from Nigeria to Niger Republic through Nigeria-Niger border. Exportation of smoked fish of about 2008.22±856.51kg (₦3,260,028.25±1,231,860.25) was recorded in Jigawa State while dried fish of 1,800.00±0.00kg (₦4,606,200±0.00) and 2,500.00±0.00kg (₦4,425,000.00±0.00) were observed among respondents in Yobe and Niger States, respectively along Nigeria-Niger border and Lake Kainjiinland fisheries. Along Nigeria-Cameroon-Chad border smoked fish of 1,278.06±0.00kg (N2,581,672.22±0.00) and 951.56±214.34kg (₦2,136,666.84±306,413.18) were exported from Benue and Borno State, respectively. In Lagos State, 20.56% (1,088.75±292.76kg) and 31.69% (441.00±241.83kg) of the total smoked and dried fish, respectively traded in the State were supplied through the cross-border trade while 14.67% (2,300.00±424.26kg) and 4.50% (150.88±97.21kg) of fresh and smoked fish, respectively were imported from Benin Republic through cross-border trade in Ogun State. The Gini coefficient value for most of the actors in the States along the Nigeria-Niger border and Lake Kainji-inland fisheries was 0.34, 0.45, 0.41, and 0.43 for wholesalers of smoked fish, retailers of spiced fish, wholesalers and retailers of frozen fish respectively. The linear regression (b) coefficient for all the forms of fish were positive except for dried and fried whose b values were -7.66 and -5.15 respectively. Gini coefficients were 0.63, 0.70, 0.71 and 0.59 for fresh, smoked, dried and frozen fish markets in the States along the Nigeria-Cameroon-Chad border and the linear regression coefficient was positive for all forms of fish. The linear regression coefficient for all forms of fish marketed along the Nigeria-Benin border were positive except for fried fish with b value of -485.89. In conclusion, the fish markets along Nigeria-Niger border, Lake Kainji-inland fisheries, the Northern part of the Nigeria-Cameroon-Chad and the Nigeria-Benin border which includes...

Foraging for wild resources links urban citizens to nature and biodiversity while providing resources important for local livelihoods and culture. However, the abundance and distributional (in)equity of forageable urban tree resources have rarely been examined. Consequently, this study assessed the abundance of forageable street trees and their distribution in Lagos metropolis, Nigeria. During a survey of 32 randomly selected wards across 16 local government areas (LGAs) in the metropolis, 4,017 street trees from 46 species were enumerated. The LGA with the highest number of street trees was Ikeja, with 818 trees, while Lagos Island had the lowest count, with two trees. This disparity in tree numbers could be attributed to variations in human population density within each LGA. Ninety-four percent of the street trees surveyed had at least one documented use and 76 % had two, and thus were potentially forageable. However, the most common species had relatively low forageability scores. Only 5.6 % of the total street tree population was rated as highly forageable, with a usability score of at least 11 out of 15. The most forageable street trees were fruit trees and non-native species. The forageable street trees in the LGAs showed a significant disparity in their distribution, as evidenced by a Gini coefficient of 0.81. Overall, richer neighbourhoods had a higher street tree abundance, richness, and forageability potential. To meet greening and foraging goals and address the current inequitable distribution, we suggest allocating more funds for greening, particularly in low-income neighbourhoods. Further research should evaluate forageable species from other sites to acquire a detailed understanding of the distribution and abundance of forageable resources in Lagos metropolis.

Improvement of food, nutritional security and poverty reduction in Africa can be addressed through better integration of intra-regional fish trade into the nation-state policy agenda. Data crucial to the development of regional fish trade needs to be obtained. However, there is paucity of information on market structure, products and value of fish trade along regional borders in Africa. This study therefore investigated fish marketing structure, the marketing actor’s characteristics, fish distribution channels, market profitability and efficiency along the Nigeria-Niger border and Lake Kainji inland fisheries. A multistage sampling procedure was used in the selection of respondents for this study. Random sampling was carried out in selecting four states Sokoto, Katsina, Jigawa and Yobe along the Nigeria-Niger border, Niger state was purposively selected based on its location in the Lake Kainji inland fisheries. Data was collected from 150 respondents in each of the states comprising 50 producers, processors and marketers each, amounting to 750 with the use of a structured questionnaire. Data on socio-economic characteristics, marketing operations, marketing channel, market structure, profitability and trade flow were obtained. Data were analysed using descriptive statistics, budgetary indices, gini coefficient, linear regression, Stochastic production frontier model and ANOVA at α 0.05. There was a predominance of male producers, marketers and processors in Katsina (100.0%, 98.0%, 98.0%), while in Niger state, processors were dominated by women (54.0%). Majority of producers (36.0%), processors (40.0%) in Sokoto state, marketers (36.0%) and processors (53.0%) in Katsina state; and processors (50.0%) in Niger were within the age of 31-40 years. The producer-consumer channel had an efficiency of 618.47 while that for producer-retailerconsumer channel was 435.85. The minimum and maximum average volume (kg) of fish traded within and across the States were for fried (882.25±339.15, 730.72±283.39) and fresh fish (1702.23±978.32; 1673.20±439.88). An average volume (kg) traded of 1386.46±760.57 for dried fish was traded across the regional border. 478.22±292.01 and 91.04±80.53 were the highest and least marketing efficiency among artisanal fishermen and retailers respectively for fresh fish. Processors had the highest average gross margin per kg (₦1157.94±492.26) while wholesalers had the least ₦387.94±363.87 for smoked fish. The Gini coefficient value for most of the actors showed partial inequality in the revenue distribution of fresh, smoked, dried, fried, spiced and frozen fish, except for wholesalers of smoked fish (0.34), retailers of spiced fish (0.45) and iii wholesalers (0.41) and retailers (0.43) of frozen fish. The linear regression b values for all the forms of fish were positive except for dried and fried whose b values were -7.66 and -5.15 respectively. The direct marketing channels were most efficient for fresh and processed fish. The market structures for most of the producers (capture), marketers and processors were monopolistic in nature and there was barrier into entry for fried and dried fish. Therefore there is need for better organization of fish markets.

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.

As of January 2024, Johann Rupert and his family are the richest people in South Africa with a net worth of 11.1 billion U.S. dollars. The Rupert family are ranked at 216 globally and are the second richest people in Africa after Nigerian billionaire, Aliko Dangote, reclaimed the title. Rupert's net worth dropped by seven million U.S. dollars from 2023, mainly due to a decline in the market value of luxury goods company Richemont, where he owns an estimated 9.14 percent stake. Nicky Oppenheimer and his family placed as the second richest in South Africa, with a net worth of 9.5 billion U.S. dollars and ranking at 276 worldwide. Their net worth source was mostly founded via the diamond market. They were followed by Koos Bekker, the chairman of media group Naspers, with 3.1 billion U.S. dollars who placed 1,133 globally. Patrice Motsepe, the first black African on the Forbes list and founder of African Rainbow Minerals, ranked 1,140 out of the global billionaires list, with a net worth of three billion U.S. dollars. Where does the wealth reside in the continent? The three largest economies on the continent in terms of Gross Domestic Product (GDP), namely Nigeria, Egypt, and South Africa saw the highest concentration of private wealth, with South Africa ranking first when it came to private wealth. In fact, out of Africa’s 20 wealthiest families and individuals, 14 of them were from these economies. Since 2010, the number of high net worth individuals on the continent fluctuated peaking at 148 individuals in 2017 and reaching its lowest in 2020 at 125. High net worth individuals are people whose net assets exceed one million U.S. dollars. On the other hand, South Africa suffered from severe income inequality ranking as the most unequal country in the world with a Gini index of 0.63 points.