Accounting for Poverty in Africa: Illustration with Survey Data from Nigeria

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Accounting for Poverty in Africa: Illustration with Survey Data from Nigeria No 149 – May 2012 Accounting for Poverty in Africa: Illustration with Survey Data from Nigeria John C. Anyanwu Editorial Committee Rights and Permissions All rights reserved. Steve Kayizzi-Mugerwa (Chair) Anyanwu, John C. Verdier-Chouchane, Audrey The text and data in this publication may be Ngaruko, Floribert reproduced as long as the source is cited. Faye, Issa Reproduction for commercial purposes is Shimeles, Abebe Salami, Adeleke forbidden. The Working Paper Series (WPS) is produced by the Development Research Department of the African Development Bank. The WPS Coordinator disseminates the findings of work in progress, Salami, Adeleke preliminary research results, and development experience and lessons, to encourage the exchange of ideas and innovative thinking among researchers, development practitioners, policy makers, and donors. The findings, interpretations, and conclusions expressed in the Bank’s WPS are entirely Copyright © 2012 those of the author(s) and do not necessarily African Development Bank represent the view of the African Development Angle de l’avenue du Ghana et des rues Bank, its Board of Directors, or the countries Pierre de Coubertin et Hédi Nouira they represent. BP 323 -1002 TUNIS Belvédère (Tunisia) Tel: +216 71 333 511 Fax: +216 71 351 933 Working Papers are available online at E-mail: [email protected] http:/www.afdb.org/ Correct citation: Anyanwu, John C, Accounting for Poverty in Africa: Illustration with Survey Data from Nigeria, Working Paper Series N° 149, African Development Bank, Tunis, Tunisia. AFRICAN DEVELOPMENT BANK GROUP Accounting for Poverty in Africa: Illustration with Survey Data from Nigeria John C. Anyanwu1 Working Paper No. 149 May 2012 Office of the Chief Economist 1 John C. Anyanwu is a Lead Research Economist at the Development Research Department, AFDB ([email protected]) Abstract Apart from presenting the poverty Moslem. The variables that are profile, this paper examines the negatively and significantly correlated correlates of poverty with multivariate with the probability of being poor are: models that predict the probability of age of the household head, quadratic of being poor using data from the Nigerian household size, residence in an urban National Consumer Survey (NCS) of area, post-secondary (tertiary) 2003/2004. The probability of a education attainment, being a Christian, household being poor was examined for and residence in the south south, the nation as a whole, as well as male- southeast, south west, and north east headed and female-headed households zones of the country. Based on the and for urban/rural geographical areas. results, we recommend a number of In particular, the variables that are policy interventions (including a broad positively and significantly correlated poverty reduction framework) necessary with the probability of being poor to reduce poverty in Nigeria and similar nationally are: household size, lack of African countries. education, residence in the North Central zone, being single, and being a Keywords: Poverty, Africa, Nigeria. JEL Classification: I32, I38 I. Introduction One of the targets for reducing extreme poverty in Africa involves halving the proportion of people living in absolute poverty from 48 percent in 1990 to 24 percent by 2015. Available data so far indicate that it is only the North African countries of Algeria, Egypt, Libya, Morocco and Tunisia as well as Mauritius that have already met this target. Available data indicate, for example, that in Sub-Saharan Africa, the $1.25 a day poverty rate has shown no sustained decline over the whole period since 1981, starting and ending at roughly 50 percent at 2008 purchasing power parity (PPP) – the highest in the world (Figure 1). Indeed, in absolute terms, the number of poor people nearly doubled from 205 million in 1981 to 386 million in 2008 (Figure 2). If current trend continues, the proportion of people living in extreme poverty in Africa as a whole would be about 39 percent by 2015 – far greater than the targeted 24 percent. Source: Author, using data from The World Bank (2012): http://siteresources.worldbank.org/INTPOVCALNET/Resources/Global_Poverty_Update_2012_02-29-12.pdf Source: Author, using data from The World Bank (2012): http://siteresources.worldbank.org/INTPOVCALNET/Resources/Global_Poverty_Update_2012_02-29-12.pdf 5 Nigeria is one of those African countries that will not be able to reach the target poverty MDG target by 2015. Successive governments in Nigeria have initiated measures aimed at poverty (rural and urban) reduction since 1980. These include: the Green Revolution (1980); programs to alleviate the pains of Structural Adjustment Program (SAP) through the Directorate of Food, Road and Rural Infrastructure (DFRRI) and the National Directorate of Employment (NDE) (1986); the People’s Bank of Nigeria (1990) (see Anyanwu and Uwatt, 1993); community banks; the Better Life Program (BLP); Family Support Program (FSP) and Family Economic Advancement Program (FEAP); establishment of National Agricultural Land Development Authority (NALDA) (1993) as well as the Agricultural Development Programs (ADP) and the Strategic Gains Reserves Programs (SGRP). Another key measure was the establishment of the Poverty Alleviation Program (PAP) (2000) which later metamorphosed into the Poverty Eradication Program (PEP) and culminated in the National Poverty Eradication Program (NAPEP) (2001). NAPEP has been organized around four schemes, namely, the Youth Empowerment Scheme (YES), Rural Infrastructure Development (RIDS), Social Welfare Schemes (SOWESS) and the National Resource Development and Conservation Scheme (NRDCS).2 We have also had periodic reviews of salaries/wages and tax rates and allowances as well as pensions for increase the purchasing power of civil and public servants. In addition, there is the Interim Poverty Reduction Strategy Paper (IPRSP) with the aim of building on the gains of PAP and PEP. One of the recent measures that attracted a lot of attention was the National Economic Empowerment and Development Strategy (NEEDS), which was built on the interim PRSP. This medium term strategy (2003-2007) derived from the long-term goals of poverty reduction, wealth creation, employment generation and value re-orientation, being a national coordinated framework of action in close collaboration with the state and local governments and other stakeholders. The main strategies were anchored on: empowering people (Social Charter or Human Development Agenda); promoting private enterprise, and changing the way the government does its work (Reform Government and Institutions). The equivalent of NEEDS at State and Local Government levels were the State Economic Empowerment and Development Strategy (SEEDS) and Local Government Economic Empowerment and Development Strategy (LEEDS). Though some of measures and reforms made some positive impacts, they proved unsustainable while at the same time failed to result in sustainable poverty reduction. A number of factors have been identified to contributing to the failure of these measures to achieve sustainable poverty reduction, including poor targeting mechanisms, lack of focus on the poor, program inconsistency, apparent disconnect between the government and the poor, poor implementation, and corruption (Adogamhe, 2010; Ugoh, and Ukpere, 2009; Ogwumike, 2002). Indeed, these earlier efforts to address poverty failed largely because they were badly implemented and had no particular focus on the poor in terms of design and implementation. Thus, an understanding of the various dimensions and determinants of poverty in Nigeria is a precondition for effective pro-poor development strategies in the country. This paper, therefore, examines the correlates of poverty with multivariate models that predict the probability of being poor using data from the Nigerian National Consumer Survey (NCS) of 2003/2004. The data covers 36 states and Abuja FCT. It comprises a large sample size of 19,158 usable households. The comparison of this data set to previous ones is summarized in Table 1. 2 See Ugoh and Ukpere (2009) and Obadan (2002) for full detailed discussion. 6 The analysis is useful, first, to verify the relative role of the various factors in determining poverty status, and second, to recommend policy changes to reduce poverty incidence in the country. The probability of a household being poor was examined for the nation as a whole, as well as male-headed and female-headed households and for urban/rural geographical areas. Table 1: Sample Sizes for NCS Data Sets, 1980 -2004 Year Sample Design Urban Rural Total Three Stages-towns, EAs, No (%) No (%) 1980 Households 5,582 54.3 4,698 45.7 10,280 1985 Two Stages- EAs, HHs 5,273 56.6 4,044 43.4 9,317 1992 Two Stages- EAs, HHs 3,978 41.0 5,719 59.0 9,697 1996 Two Stages- EAs, HHs 3,037 21.1 11,358 78.9 14,395 2003/ Two Stages- EAs, HHs 4,646 24.2 14,512 75.8 19,158 2004 Source: Federal Office Statistics (1999), National Bureau of Statistics (2005) and NBS Data Files. Thus the further contents of the paper can therefore be adumbrated as follows. Section II discusses the incidence of poverty in Nigeria using the nationwide survey results (2003/2004) while Section III presents the empirical estimates of the determinants of poverty in Nigeria. In section IV, first, a regression for the entire country was estimated, second, geographic area regressions were estimated (urban and rural), third, the probability of being poor is presented for male-headed and female-headed (gender) households. Section V concludes the paper with policy implications. II. Nigeria’s Poverty Profile: Trend and Dimension 2.1. Trend in Poverty Incidence Table 2 shows the national levels and population in poverty from 1980 to 2010 - see also Figure 3. Starting from 28.1 per cent in 1980, national poverty reached 66.9 per cent in 1996 before falling to 54.4 per cent in 2003/2004 – and then reaching a peak in 2010 to 69 per cent. However the population in poverty continues to rise – from 18.3 million in 1980 to 68.7 million in 2003/2004 and 112.5 million in 2010.
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