Historical dataset showing Nigeria poverty rate by year from 1985 to 2018.

Yearly (annual) dataset of the Nigeria Poverty Rate, including historical data, latest releases, and long-term trends from 1985-12-31 to 2018-12-31. Available for free download in CSV format.

Actual value and historical data chart for Nigeria Poverty Headcount Ratio At National Poverty Line Percent Of Population

Nigeria NG: Poverty Headcount Ratio at National Poverty Lines: % of Population data was reported at 46.000 % in 2009. This records a decrease from the previous number of 48.400 % for 2003. Nigeria NG: Poverty Headcount Ratio at National Poverty Lines: % of Population data is updated yearly, averaging 47.200 % from Dec 2003 (Median) to 2009, with 2 observations. The data reached an all-time high of 48.400 % in 2003 and a record low of 46.000 % in 2009. Nigeria NG: Poverty Headcount Ratio at National Poverty Lines: % of Population 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. National poverty headcount ratio is the percentage of the population living below the national poverty lines. National estimates are based on population-weighted subgroup estimates from household surveys.; ; World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.; ; This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

71,0 (%) in 2018. Poverty headcount ratio at $3.20 a day is the percentage of the population living on less than $3.20 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.

As of 2025, an estimated population of over ***** million in Nigeria lived in extreme poverty, with the poverty threshold at **** U.S. dollars a day. This stood as an increase from the headcount of about ***** million recorded for the previous year.

Number of poor at $1.9 a day of Nigeria slumped by 12.19% from 89.4 million persons in 2009 to 78.5 million persons in 2018. Since the 5.43% surge in 2003, number of poor at $1.9 a day rose by 3.70% in 2018. Number of people, in millions, living on less than $1.90 a day at 2011 PPP is calculated by multiplying the poverty rate and the population. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.

12.5 (%) in 2018. Poverty gap at $1.90 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $1.90 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. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.

Abstract

The main objectives of the 2018/19 NLSS are: i) to provide critical information for production of a wide range of socio-economic and demographic indicators, including for benchmarking and monitoring of SDGs; ii) to monitor progress in population’s welfare; iii) to provide statistical evidence and measure the impact on households of current and anticipated government policies. In addition, the 2018/19 NLSS could be utilized to improve other non-survey statistical information, e.g. to determine and calibrate the contribution of final consumption expenditures of households to GDP; to update the weights and determine the basket for the national Consumer Price Index (CPI); to improve the methodology and dissemination of micro-economic and welfare statistics in Nigeria.

The 2018/19 NLSS collected a comprehensive and diverse set of socio-economic and demographic data pertaining to the basic needs and conditions under which households live on a day to day basis. The 2018/19 NLSS questionnaire includes wide-ranging modules, covering demographic indicators, education, health, labour, expenditures on food and non-food goods, non-farm enterprises, household assets and durables, access to safety nets, housing conditions, economic shocks, exposure to crime and farm production indicators.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Communities

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2018/19 NLSS sample is designed to provide representative estimates for the 36 states and the Federal Capital Territory (FCT), Abuja. By extension. The sample is also representative at the national and zonal levels. Although the sample is not explicitly stratified by urban and rural areas, it is possible to obtain urban and rural estimates from the NLSS data at the national level. At all stages, the relative proportion of urban and rural EAs as has been maintained.

Before designing the sample for the 2018/19 NLSS, the results from the 2009/10 HNLSS were analysed to extract the sampling properties (variance, design effect, etc.) and estimate the required sample size to reach a desired precision for poverty estimates in the 2018/19 NLSS.

EA SELECTION: The sampling frame for the 2018/19 NLSS was based on the national master sample developed by the NBS, referred to as the NISH2 (Nigeria Integrated Survey of Households 2). This master sample was based on the enumeration areas (EAs) defined for the 2006 Nigeria Census Housing and Population conducted by National Population Commission (NPopC). The NISH2 was developed by the NBS to use as a frame for surveys with state-level domains. NISH2 EAs were drawn from another master sample that NBS developed for surveys with LGA-level domains (referred to as the “LGA master sample”). The NISH2 contains 200 EAs per state composed of 20 replicates of 10 sample EAs for each state, selected systematically from the full LGA master sample. Since the 2018/19 NLSS required domains at the state-level, the NISH2 served as the sampling frame for the survey.

Since the NISH2 is composed of state-level replicates of 10 sample EAs, a total of 6 replicates were selected from the NISH2 for each state to provide a total sample of 60 EAs per state. The 6 replicates selected for the 2018/19 NLSS in each state were selected using random systematic sampling. This sampling procedure provides a similar distribution of the sample EAs within each state as if one systematic sample of 60 EAs had been selected directly from the census frame of EAs.

A fresh listing of households was conducted in the EAs selected for the 2018/19 NLSS. Throughout the course of the listing, 139 of the selected EAs (or about 6%) were not able to be listed by the field teams. The primary reason the teams were not able to conduct the listing in these EAs was due to security issues in the country. The fieldwork period of the 2018/19 NLSS saw events related to the insurgency in the north east of the country, clashes between farmers and herdsman, and roving groups of bandits. These events made it impossible for the interviewers to visit the EAs in the villages and areas affected by these conflict events. In addition to security issues, some EAs had been demolished or abandoned since the 2006 census was conducted. In order to not compromise the sample size and thus the statistical power of the estimates, it was decided to replace these 139 EAs. Additional EAs from the same state and sector were randomly selected from the remaining NISH2 EAs to replace each EA that could not be listed by the field teams. This necessary exclusion of conflict affected areas implies that the sample is representative of areas of Nigeria that were accessible during the 2018/19 NLSS fieldwork period. The sample will not reflect conditions in areas that were undergoing conflict at that time. This compromise was necessary to ensure the safety of interviewers.

HOUSEHOLD SELECTION: Following the listing, the 10 households to be interviewed were selected from the listed households. These households were selected systemically after sorting by the order in which the households were listed. This systematic sampling helped to ensure that the selected households were well dispersed across the EA and thereby limit the potential for clustering of the selected households within an EA.

Occasionally, interviewers would encounter selected households that were not able to be interviewed (e.g. due to migration, refusal, etc.). In order to preserve the sample size and statistical power, households that could not be interviewed were replaced with an additional randomly selected household from the EA. Replacement households had to be requested by the field teams on a case-by-case basis and the replacement household was sent by the CAPI managers from NBS headquarters. Interviewers were required to submit a record for each household that was replaced, and justification given for their replacement. These replaced households are included in the disseminated data. However, replacements were relatively rare with only 2% of sampled households not able to be interviewed and replaced.

Sampling deviation

Although a sample was initially drawn for Borno state, the ongoing insurgency in the state presented severe challenges in conducting the survey there. The situation in the state made it impossible for the field teams to reach large areas of the state without compromising their safety. Given this limitation it was clear that a representative sample for Borno was not possible. However, it was decided to proceed with conducting the survey in areas that the teams could access in order to collect some information on the parts of the state that were accessible.

The limited area that field staff could safely operate in in Borno necessitated an alternative sample selection process from the other states. The EA selection occurred in several stages. Initially, an attempt was made to limit the frame to selected LGAs that were considered accessible. However, after selection of the EAs from the identified LGAs, it was reported by the NBS listing teams that a large share of the selected EAs were not safe for them to visit. Therefore, an alternative approach was adopted that would better ensure the safety of the field team but compromise further the representativeness of the sample. First, the list of 788 EAs in the LGA master sample for Borno were reviewed by NBS staff in Borno and the EAs they deemed accessible were identified. The team identified 359 EAs (46%) that were accessible. These 359 EAs served as the frame for the Borno sample and 60 EAs were randomly selected from this frame. However, throughout the course of the NLSS fieldwork, additional insurgency related events occurred which resulted in 7 of the 60 EAs being inaccessible when they were to be visited. Unlike for the main sample, these EAs were not replaced. Therefore, 53 EAs were ultimately covered from the Borno sample. The listing and household selection process that followed was the same as for the rest of the states.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two sets of questionnaires – household and community – were used to collect information in the NLSS2018/19. The Household Questionnaire was administered to all households in the sample. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

Household Questionnaire: The Household Questionnaire provides information on demographics; education; health; labour; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; agriculture and land tenure; and other sources of household income.

Community Questionnaire: The Community Questionnaire solicits information on access to transported and infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Cleaning operations

CAPI: The 2018/19 NLSS was conducted using the Survey Solutions Computer Assisted Person Interview (CAPI) platform. The Survey Solutions software was developed and maintained by the Development Economics Data Group (DECDG) at the World Bank. Each interviewer and supervisor was given a tablet

Nigeria NG: Proportion of Population Pushed Below the $3.20: Poverty Line by Out-of-Pocket Health Care Expenditure: 2011 PPP: % data was reported at 1.080 % in 2018. This records a decrease from the previous number of 1.883 % for 2015. Nigeria NG: Proportion of Population Pushed Below the $3.20: Poverty Line by Out-of-Pocket Health Care Expenditure: 2011 PPP: % data is updated yearly, averaging 2.898 % from Dec 2003 (Median) to 2018, with 6 observations. The data reached an all-time high of 3.968 % in 2003 and a record low of 1.080 % in 2018. Nigeria NG: Proportion of Population Pushed Below the $3.20: Poverty Line by Out-of-Pocket Health Care Expenditure: 2011 PPP: % 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: Social: Poverty and Inequality. Proportion of population pushed below the $3.20 ($2011 PPP) poverty line by out-of-pocket health care expenditure. This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the $3.20 poverty line, but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).; ; World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021.; Weighted Average; This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/].

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.

78.5 (million persons) in 2018. Number of people, in millions, living on less than $1.90 a day at 2011 PPP is calculated by multiplying the poverty rate and the population. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.

Abstract

The Nigeria IDP Survey was conducted in IDP camps and host communities in the Northeast regions of Nigeria. The survey was used to compile a socio-economic profile of IDPs and host communities to help inform durable solutions to internal displacement. The survey contains information on poverty, education, service access, employment and livelihoods, as well as perceptions. The data combines detailed household information with displacement-specific information including drivers of displacement, access to resettlement mechanisms, and return intentions. It also includes comprehensive information on assets and consumption, to allow estimation of poverty based on the Rapid Consumption methodology.

Geographic coverage

The survey covered IDPs in camps, IDPs in host communities, and host communities in six states in Northeast Nigeria. The six states are Adamawa, Bauchi, Borno, Gombe, Taraba, and Yobe. The survey thus represents IDPs (in camps and in host communities)) and host communities of Northeast Nigeria.

Analysis unit

Household, individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The Nigeria IDP Survey is a household survey with a multi-stage stratified random sample. Six Northeastern states were covered: Adamawa, Bauchi, Borno, Gombe, Taraba, and Yobe. The sampling frame consisted of a list of wards with IDP household counts in the six states, provided by the International Organization for Migration (IOM) Displacement Tracking Matrix (DTM) 2017.

The sample had 6 strata with each state as a stratum. Each stratum was divided into grids of 150 by 150 meters using GIS technology defining the enumeration areas (EAs). The number of EAs to be selected from each stratum was obtained proportional to the IDP population sizes in each state. For the IDP sample, the EAs consisted of two types of IDP settlements, which were host community settings and camp settings. A total of 111 EAs across the 6 states were randomly selected proportional to size. Out of these, 77 EAs were host community type EAs and 48 were camp-like EAs. For the host community sample of 1,500 households, EAs were restricted to the 77 host community type EAs that were selected based on IDPs population numbers, in the absence of residential population estimates at this level. The survey is representative for IDPs and host communities, defining host communities as the non-displaced population living in the EAs with displaced populations.

All the households in the selected EAs were first listed, and 12 IDP households and 12 (or multiples of it) host community households were randomly selected and surveyed per EA, to reach the designated sample size.

Sampling deviation

Certain adaptations had to be made during the listing and survey period. Households with duplicate names and contact numbers and households outside the cluster boundaries were dropped. A few EAs also had to be dropped from the sampling due to insecurity or – in occasional cases – due to unreachability due to inclement weather over prolonged periods of time. While EAs were initially replaced at the beginning of the survey, new replacements were avoided at the end of the survey duration to avoid extended field work time due to cost implications.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire contains modules on Household Member Roster, Household Characteristics, Food Consumption, Non food consumption, Livestock, Durable Goods, Wellbeing and Opinions, and Displacement. The questionnaire is available for download with the dataset.

Cleaning operations

See accompanying Stata do-files, available under the related materials tab.

12,5 (%) in 2018. Poverty gap at $1.90 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $1.90 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. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.

Proportion of Population Pushed Below the 60% Median Consumption Poverty Line By Out-of-Pocket Health Expenditure：百分比在12-01-2018达2.700%，相较于12-01-2012的3.280%有所下降。Proportion of Population Pushed Below the 60% Median Consumption Poverty Line By Out-of-Pocket Health Expenditure：百分比数据按年更新，12-01-2003至12-01-2018期间平均值为3.010%，共5份观测结果。该数据的历史最高值出现于12-01-2003，达4.310%，而历史最低值则出现于12-01-2009，为2.270%。CEIC提供的Proportion of Population Pushed Below the 60% Median Consumption Poverty Line By Out-of-Pocket Health Expenditure：百分比数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的尼日利亚 – Table NG.World Bank.WDI: Social: Poverty and Inequality。

The study examined child development and family human capital investment decisions in Nigeria. The study focused on household per capita income and family structure using the Nigeria living standard survey for 2018/2019 for the secondary data analysis and a field survey conducted by the researchers in six states in each of the geopolitical zones in Nigeria for the primary data analysis. The study was anchored on household utility maximization theory using the ordinary least squares (OLS) method to analyse the secondary data. Four different results were obtained. First, the result of findings from the OLS estimate revealed that per capita income had no significant impact on Family Human Capital Investment Decisions (FHCID) and male perception of the cost of education had a significant positive impact on FHCID. On the contrary, multi-dimensional poverty index and female perception of the cost of education had an inverse significant impact on FHCID. The second result revealed that average household size, family residence from 1 to 30 minutes proximity to school and 31 minutes and above proximity to school had no significant impact on FHCID. Dependency ratio showed an inverse significant impact on FHCID and family literacy level showed a significant positive impact on FHCID. The third result from the binary logistics regression showed that age, occupation and place of residence of the household head had no significant impact on FHCID. Gender (female-headed household) and education showed an inverse significant impact on FHCID. However, household head years in business or paid employment showed a positive impact on FHCID. The fourth result from the binary logistics regression revealed that marital status had no significant impact on FHCID; family size had a significant negative impact on FHCID; and family structure (type of parents) and number of girl child in the household had a direct impact on FHCID. This study showed complementarities in the home utility function, such that the marginal product of investments rises as family living standards rise. These findings highlight lifetime inequalities and necessitates a special focus on treatments for low-income households. Understanding human capital development and how diverse elements interact is critical to combating poverty and its intergenerational transmission. As a result, this study made several recommendations. First, the importance of persistent action by the government and other donor agencies such as the United Nation Children’s Fund (UNICEF) and The World Bank to address the problems of income inequality and pervasive poverty ravaging Nigeria’s economy. The study strongly recommends that family, especially parents, maintain justice and fairness within the home, to foster constructive, sympathetic and peaceful home, encouraging most children to exhibit excellent academic performance. Third, government agencies and hospitals, especially in rural areas, intensify family planning and birth control campaign to help reduce household size. Fourth, children of the poor be given opportunities for paid employment, to enhance their performance in the school. Fifth, children from poor homes be provided with access to scholarships, free instructional materials and books. Sixth, government and its agencies on education intensify sensitization and campaign for families to embrace Western education, especially in the northern region, promote compulsory primary basic education for all children and prosecute parents of out-of-school children or child labour to serve as a deterrent to others. Finally, the study recommends that non-governmental and religious organizations preach peace and tolerance within the family for the well-being and human capital development of their wards.

（停止更新）人口被推低至3.20美元以下的比例：自付医保贫困线：2011年购买力平价：百分比在12-01-2018达1.080%，相较于12-01-2015的1.883%有所下降。（停止更新）人口被推低至3.20美元以下的比例：自付医保贫困线：2011年购买力平价：百分比数据按年更新，12-01-2003至12-01-2018期间平均值为2.898%，共6份观测结果。该数据的历史最高值出现于12-01-2003，达3.968%，而历史最低值则出现于12-01-2018，为1.080%。CEIC提供的（停止更新）人口被推低至3.20美元以下的比例：自付医保贫困线：2011年购买力平价：百分比数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的尼日利亚 – Table NG.World Bank.WDI: Social: Poverty and Inequality。

Nigeria’s inflation has been higher than the average for African and Sub-Saharan countries for years now, and even exceeded 16 percent in 2017 – and a real, significant decrease is nowhere in sight. The bigger problem is its unsteadiness, however: An inflation rate that is bouncing all over the place, like this one, is usually a sign of a struggling economy, causing prices to fluctuate, and unemployment and poverty to increase. Nigeria’s economy - a so-called “mixed economy”, which means the market economy is at least in part regulated by the state – is not entirely in bad shape, though. More than half of its GDP is generated by the services sector, namely telecommunications and finances, and the country derives a significant share of its state revenues from oil. Because it got highTo simplify: When the inflation rate rises, so do prices, and consequently banks raise their interest rates as well to cope and maintain their profit margin. Higher interest rates often cause unemployment to rise. In certain scenarios, rising prices can also mean more panicky spending and consumption among end users, causing debt and poverty. The extreme version of this is called hyperinflation: A rapid increase of prices that is out of control and leads to bankruptcies en masse, devaluation of money and subsequently a currency reform, among other things. But does that mean that low inflation is better? Maybe, but only to a certain degree; the ECB, for example, aspires to maintain an inflation rate of about two percent so as to keep the economy stable. As soon as we reach deflation territory, however, things are starting to look grim again. The best course is a stable inflation rate, to avoid uncertainty and rash actions. Nigeria todayNigeria is one of the countries with the largest populations worldwide and also the largest economy in Africa, with its economy growing rapidly after a slump in the aforementioned year 2017. It is slated to be one of the countries with the highest economic growth over the next few decades. Demographic key indicators, like infant mortality rate, fertility rate, and the median age of the population, all point towards a bright future. Additionally, the country seems to make big leaps forward in manufacturing and technological developments, and boasts huge natural resources, including natural gas. All in all, Nigeria and its inflation seem to be on the upswing – or on the path to stabilization, as it were.

As of July 2024, Nigeria's population was estimated at around 229.5 million. Between 1965 and 2024, the number of people living in Nigeria increased at an average rate of over two percent. In 2024, the population grew by 2.42 percent compared to the previous year. Nigeria is the most populous country in Africa. By extension, the African continent records the highest growth rate in the world. Africa's most populous country Nigeria was the most populous country in Africa as of 2023. As of 2022, Lagos held the distinction of being Nigeria's biggest urban center, a status it also retained as the largest city across all of sub-Saharan Africa. The city boasted an excess of 17.5 million residents. Notably, Lagos assumed the pivotal roles of the nation's primary financial hub, cultural epicenter, and educational nucleus. Furthermore, Lagos was one of the largest urban agglomerations in the world. Nigeria's youthful population In Nigeria, a significant 50 percent of the populace is under the age of 19. The most prominent age bracket is constituted by those up to four years old: comprising 8.3 percent of men and eight percent of women as of 2021. Nigeria boasts one of the world's most youthful populations. On a broader scale, both within Africa and internationally, Niger maintains the lowest median age record. Nigeria secures the 20th position in global rankings. Furthermore, the life expectancy in Nigeria is an average of 62 years old. However, this is different between men and women. The main causes of death have been neonatal disorders, malaria, and diarrheal diseases.

每天生活费不足2.15美元的贫困人口比例：2017 PPP：占人口百分比在12-01-2018达30.900%，相较于12-01-2015的32.300%有所下降。每天生活费不足2.15美元的贫困人口比例：2017 PPP：占人口百分比数据按年更新，12-01-1985至12-01-2018期间平均值为41.350%，共8份观测结果。该数据的历史最高值出现于12-01-1996，达58.400%，而历史最低值则出现于12-01-2018，为30.900%。CEIC提供的每天生活费不足2.15美元的贫困人口比例：2017 PPP：占人口百分比数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的尼日利亚 – Table NG.World Bank.WDI: Social: Poverty and Inequality。