Department of Geography,

Slides:



Advertisements
Similar presentations
June 2009 Feedback to States and Zone Routine Immunization Performance Data as at 15 th July 2009 in National Database.
Advertisements

December 2009 Feedback to States and Zone Routine Immunization Performance Data as at 15 th January 2010 in National Database.
Qtr1 (Jan-Mar 2009) Feedback to States and Zone Routine Immunization Performance Data as at 15 th April 2009 in National Database.
The Wealth Index MICS3 Data Analysis and Report Writing Workshop.
MEASURING LABOUR FORCE PARTICIPATION OF WOMEN
THE 2004 LIVING CONDITIONS MONITORING SURVEY : ZAMBIA EXTENT TO WHICH GENDER WAS INCORPORATED presented at the Global Forum on Gender Statistics, Accra.
ESA/STAT/AC.219/15 Survey Analysis for Gender Indicators Sulekha Patel Development Data Group World Bank Manila October 11, 2010 ESA/STAT/AC.219/15.
Lisa Dubay, Ph.D., Sc.M. Johns Hopkins Bloomberg School of Public Health and Center for Children and Families Getting to the Finish Line:
REGIONAL EDUCATION INDICATORS PROJECT Progress towards the achievement of the Summit of the Americas Goals November 14, 2007.
The Role of Employment for Growth and Poverty Reduction PREM learning week 2007 Catalina Gutierrez Pieter Serneels.
Spatial The World Bank Reshaping Economic Geography Priorities for Territorial Integration Somik V. Lall The World Bank European Commissions Open.
Alaska Accountability Adequate Yearly Progress January 2008, Updated.
Alaska Accountability Adequate Yearly Progress February 2007, Updated.
S2: Youth Unemployment S2.1 Economic status of young men and women aged S2.2 Regional variations in unemployment and variations within regions S2.3.
Demographics and Market Segmentation: China and India
Health Status in Los Angeles County Examining Health and Demographic Data by Service Planning Area (SPA) Anna Rose Steiner Introduction to Geographic Information.
1 The Social Survey ICBS Nurit Dobrin December 2010.
The Nature of the Bias When Studying Only Linkable Person Records: Evidence from the American Community Survey Adela Luque (U.S. Census Bureau) Brittany.
Are Area-Based Deprivation Indices A Nonsense? Dennis Pringle Dept. of Geography, NUI Maynooth; National Institute For Regional And Spatial Analysis; and.
Ana Marr, University of Greenwich, London, UK Julian Schmied, Potsdam University, Germany Third European Research Conference on Microfinance, Norway, June.
WELCOME TO POVERTY POLICY WEEK, 25-27NOV.2013
NIGERIA: THE ROLE OF DEVELOPMENT PARTNERS IN STRENGTHENING THE NATIONAL STATISTICAL SYSTEM By Alain Gaugris, DECDG WORLD BANK National Statistical User.
Poverty and Health BCHLA Webinar Dr. Brian O’Connor, MD, MHSc April 17, 2013.
Profiles of the Adolescents and Youths in Bangladesh Syeda Sitwat Shahed Narayan Das Research and Evaluation Division, BRAC 7 February, 2012.
The geography of advantage and disadvantage for older Australians: insights from spatial microsimulation JUSTINE MCNAMARA, CATHY GONG, RIYANA MIRANTI,
GHANA’S POVERTY PROFILE 2013
Providing Insights that Contribute to Better Health Policy Trends in the Uninsured: Impact and Implications of the Current Economic Environment Len Nichols,
Prepared by Kim Gilchrist Epidemiologist Public Health, MLHD May 2013 Socio-economic Disadvantage.
FORMERLY HOUSING-INSECURE FAMILIES IN SUBSIDIZED HOUSING: Julie Lowell, Ph.D. November 12, 2014 An exploratory study of family well-being after experiencing.
1 Measurement and Analysis of Poverty in Jordan Joint Study by :  Ministry of Social Development  Department of Statistics  Department for Int’l Development.
ICES 3° International Conference on Educational Sciences 2014
Tanzania poverty update Poverty Monitoring Group (PMG) September 4, 2014.
1 Assessing the Effect of Rent Control on Homelessness Grimes, Paul W. & Chressanthis, George A. “Assessing the Effect of Rent Control on Homelessness.”
1 Good Shepherd Lutheran Church The map on the left provides an illustration of the population per square kilometre in the Moncton census tracts. The map.
DATE: 26 TH AUGUST 2013 VENUE: LA PALM ROYALE BEACH HOTEL BACKGROUND OF GHANA LIVING STANDARDS SURVEY (GLSS 6) 1.
By Sanjay Kumar, Ph.D National Programme Officer (M&E), UNFPA – India
POVERTY PRESENTATION AT UNDP OFFICE POVERTY STATUS AND TREND IN TANZANIA MAINLAND, /12 Presented by Sango A. H. Simba National Bureau of Statistics.
Human Capacity Development and Maternal Mortality Reduction: Distribution and Population Coverage of Obstetricians and Gynecologists in Nigeria BY Agboghoroma.
Effects of Income Imputation on Traditional Poverty Estimates The views expressed here are the authors and do not represent the official positions.
Broadband Needs, Challenges, and Opportunities in Rural America Presented to the Rural Broadband Workshop Federal Communications Commission March 19, 2014.
If this is the answer what is the question?
Integrating a Gender Perspective into Statistics Selected topic: Poverty Statistics S. Nunhuck Statistics Mauritius.
Identifying Vulnerable Populations to Hurricanes in the city of Tampa, Florida Dr. Jennifer Collins and Dustin Hinkel Department of Geography, The University.
Reassessment of poverty status and performance of poverty alleviation measures in China Funing Zhong, Hua Liu & Qi Miao College of Economics & Management.
Old Louisville by the Numbers A Statistical Profile by Michael Price Urban Studies Institute University of Louisville Spring 2006.
Using ArcView to Create a Transit Need Index John Babcock GRG394 Final Presentation.
Workshop 2 – Integrated development in cities, rural and specific regions TiPSE – Territorial Dimensions of Poverty and Social Exclusion Petri Kahila ESPON.
Poverty measurement: experience of the Republic of Moldova UNECE, Measuring poverty, 4 May 2015.
Demographic Trends: Carl Onubogu. Average household income Percentage of population over 25 with less than high school education Percentage.
Fourth Annual Meeting of NTA Project University of California in Berkeley January 2007 CONSTRUCTION OF NATIONAL TRANSFER ACCOUNTS FOR INDIA: METHODS,
The Geography of HIV in Harris County, Texas,
An ecological analysis of crime and antisocial behaviour in English Output Areas, 2011/12 Regression modelling of spatially hierarchical count data.
Statistics Division Beijing, China 25 October, 2007 EC-FAO Food Security Information for Action Programme Side Event Food Security Statistics and Information.
Impact of Household Income on Energy Patterns in Botswana: Implications for Economic Growth and Forest Biodiversity Conservation. Charity K. Kerapeletswe.
AN OVERVIEW OF PHARMACOVIGILANCE OF HIV/AIDS IN NIGERIA
The Spatial Patterns Of Earthquake Casualties (Damages) And Social Vulnerability Zahra Golshani Natural Resource & Environmental Science University of.
Demographics 1.Studying Population 2.Population Pyramids 3.Global Village.
Voter Turnout in Texas FEBRUARY 2, Not Everyone Votes.
Residential Segregation: A Key Connector Between Race and Environmental Health Disparities Jennifer Davis, Sacoby Wilson, Muhammad Salaam, Rahnuma Hassan.
ECOSOC Thematic Discussion on Multidimensional Poverty
Mapping MPI and Monetary Poverty: The Case of Uganda
WATER AND SANITATION SECTOR IN NIGERIA
chudi s. ubosi – fnivs mrics
Demographic and Socio-Economic Profiles that Relate to Political Party Affiliation Examined in Massachusetts and Wyoming for the 2016 Presidential Election.
10: Leisure at an International Scale: Sport
Oluwole FAJEMISIN 22nd July 2014
2019 Humanitarian Need Overview Briefing
EPIDEMIOLOGICAL BURDEN OF TUBERCULOSIS IN NIGERIA August 2003
MNCH2 Learning Event on accountability: Break-out session 1 Establishing sustainable health care financing policies and structures: overcoming barriers.
Number of Trainees and Adopters of
Presentation transcript:

Department of Geography, The Geography of Poverty in Nigeria: Patterns, Determinants and Policy Implications Tolulope O. Osayomi Department of Geography, University of Ibadan, Ibadan, Nigeria.

Background Minot and Baulch(2005) ‘s study of spatial pattern of poverty in Vietnam employed the two indices: poverty incidence and poverty density. The mapping revealed two distinct spatial patterns. The interpretation of these was : “the most poor people do not live in the poor areas…”(Minot and Baulch, 2005). The import of this is that a balance must struck in alleviating ‘poor people’ and ‘poor areas’. However, the study was silent on the possible factors influencing the two different spatial patterns.

The Research Problem In Nigeria, the national poverty incidence rate is 78.3 %(National Bureau of Statistics, 2009). There are noticeable high levels of poverty concentration in 21 states (out of the 36 states in Nigeria) whose rates exceed the national average. Attention needs to be drawn to the need for a more detailed research to unravel the factors behind spatial heterogeneity.

Research Questions The paper therefore attempts to answer the following questions using Minot and Baulch (2005) approach: Can these two indices of poverty: poverty incidence and poverty density generate contrasting spatial patterns of poverty in Nigeria? What are the significant determinants of these contrasting patterns if any? What is the specific contribution of structural factors to poverty in Nigeria? What are the policy implications of the findings?

Methodology Employed the stepwise regression method to identify the significant predictors of poverty at three geographical scales of analysis: national, urban and rural. Three sets of variables: demographic/household; social and political/economic factors. Previous research on poverty in Nigeria is silent on the contribution of structural factors. Poverty incidence based on the poverty headcount of year 2004. poverty density derived by multiplying the poverty incidence by the population of the state and dividing by the areal size of the state. Measured as number of poor people per square kilometre.

Spatial pattern of poverty in Nigeria

Spatial pattern of poverty in Nigeria (2) Poverty incidence is highest in Ekiti, Bayelsa, Borno, Ebonyi, Kwara and lowest in Jigawa, Oyo, Osun. Poverty density is highest in Lagos, Kano, Ekiti, Imo, Enugu, Abia and lowest in Zamfara, Yobe, Niger, Kwara. Generally, areas of high poverty incidence do not coincide with areas of high poverty density. This is confirmed by the Spearman’s Rho (r= -0.194; p>0.05). In the language of Minot and Baulch(2005), some areas would record high poverty incidence because of the low population sizes. Two distinct geographies of poverty.

Study variables Demographic and household: household (average number of persons per household), number of children (percent of children under the age of 15), number of elderly persons (percent of elderly 65 years and above), household income ( percent of low income households), unemployment ( percent of unemployed persons), percentage ownership of radio, television and mobile phone. Political and economic: legislative representation( number of seats in the Federal House of Representatives), budgetary allocation( allocations from the Federal government from January- June, 2007) Geographic: distance from the Federal capital, Abuja, urbanization( population density as a surrogate). Social: social capital ( surrogate: percent of voters’ turnout at the 2003 presidential election), access to social services (the number of health facilities)

Results (National) Poverty incidence: Legislative representation (-0.445) R2= 20%; p < 0.05 Poverty density Urbanization (0.976) Number of children (-0.047) R2= 99.3%; p < 0.05

Results (Urban) Poverty incidence Legislative representation (-0.503) R2= 25%; p < 0.05. Poverty density Legislative representation (0.754) Number of children (-0.57) R2= 58%; p < 0.05

Results (Rural) Poverty incidence Low income households (0.625) Household size (-0487) R2= 55%; p < 0.05 Poverty density Legislative representation(1.524) Budgetary allocation (-1.189) R2= 58%; p < 0.05.

Conclusion and Policy Implications Poverty in Nigeria is largely structural in nature. Future poverty reduction efforts must appreciate this. Given the distinct geographies of poverty, there is a need for place based poverty alleviation policies which would take into the consideration the uniqueness of certain locations. Poverty density index should be considered in poverty mapping so as to have a full and richer understanding of the poverty situation.

THANK YOU!