Assessing Income Inequality in North-Eastern Nigeria

Transcription

1 Assessng Income Inequalty n North-Eastern Ngera Omotola Aderonke Mary Kabr Kayode Salman Department of Agrcultural Economcs, Unversty of Ibadan, Ibadan, Ngera Abstract Ths study nvestgates ncome nequalty n rural North-East Ngera (NEN), usng the Harmonzed Ngeran Lvng Standards Survey (HNLSS) data of 2009/2010. Data were analyzed usng; descrptve statstcs, Gn ndex and Tobt regresson methods. The results ndcate that most of the respondents were n ther mddle age accountng for 35.2% and approxmately 68 % wth no formal educaton. Inequalty among rural folks as reflected by the Gn ndex of 0.85 s very hgh, The tobt results shows that household sze, educaton, age and martal status were all sgnfcant at 1% levels. It s therefore recommended that Government should provde an enablng envronment for learnng to help household mprove on ther human captal for a sustaned poverty reducton. Keywords: Inequalty, North- east Ngera, Poverty, Rural, Tobt model. 1. Introducton/problem statement Increasng level of poverty and ncome nequalty has been a major concern among economsts and polcy experts because they are the major factors hnderng the development of any naton. Many countres vew economc growth as the leadng ndcator of poverty reducton through reduced unemployment, ncreased household ncome and reduced nequalty. In Afrca, poverty remans a burden that undermnes development because t s deep rooted and pervasve (Igbatayo and lgbnedon, 2006). Perhaps, nowhere else n the Afrcan contnent s the poverty ncdence more prevalent than n Sub- Saharan Afrcan, where about one sxth of the people are chroncally poor (Word Bank, 1996; CFA, 2005). Emprcal evdence shows that developng countres acheved a 39.2 percent reducton n the percentage of ther populaton below US$1 (PPP) per day from 1990 to Ths sgnfcant average gan was, however, not evenly dstrbuted across the developng world. The largest reductons were acheved by Eastern Asa wth 67.3 percent, followed by Southern Asa wth a reducton of 28.2 percent, whle the correspondng reducton rate for Latn and the Carbbean was 22.2 percent. Sub-Saharan Afrca, whch had the hghest level of poverty n 1990 at 46.8 percent merely managed to reduce t to 41.1 percent n 2004, havng acheved the lowest rate of reducton of 12.2 percent over the perod. Ths s a clear ndcaton that poverty dd not respond apprecably to economc growth n Sub-Saharan Afrca. (WorldBank, 2006). To reverse ths trend, many Sub-Saharan Afrcan -countres from the early 1980s ntated and mplemented the IMF World Bank Structural Adjustment Programmes (SAP). These programmes have been reported to have stmulated growth n most of these developng countres. However, n some other countres, there has been lttle or no change n terms of growth and poverty reducton. A smlar pattern can be observed n Ngera n terms of nequtable ncome dstrbuton. Despte government spendng a huge amount on varous programmes, ncludng poverty eradcaton, ncome nequalty s stll worse off. These programmes have been sparngly unsuccessful due to wdespread corrupton n publc offces. The people are stll consdered to be poor as the Natonal Bureau of Statstcs fgures ndcate that natonal poverty ncdence reduced from approxmately 65.6 percent to about 54.4 percent between 1996 and However, wth ncreases n populaton from an estmated 115 mllon to 140 mllon between the two perods, t shows that there was an ncrease n the number of people n absolute poverty from 75.4 mllon to 76.2 mllon between the two perods. Smlarly, ncome poverty moved up from 28.1 percent n 1980 to 65.6 percent n 1996 before t returned to 54.4 percent n 2004, and ncreased to 69 per cent n One of the strateges whch have been used n reducng the level of poverty and nequalty n most developng countres ncludng Ngera s the economc growth strategy whch focuses on the macro and mcroeconomc polcy whch ensures rapd growth of the economy. Economc growth s regarded as crucal as t would generate ncome earnng opportuntes for the poor and thereby make use of ther most abundant asset whch s ther labour. Besdes, human captal, the product of educaton and mprovement of health, s also crucal to rasng the lvng standard by rasng productvty, stmulatng growth and by openng up economc opportuntes to more people, whch contrbutes to reducng ncome nequalty. It encompasses nequaltes n opportuntes and nequaltes n outcomes. The UNDP (2009) descrbes nequalty n Ngera as a stuaton n whch opportuntes for upward moblty are very lmted; t means few decent jobs, poor ncome and low purchasng power for the employed. It also means poor nfrastructure and nsttutonal falure n key sectors ncludng educaton, health and transportaton etc. There has not been sgnfcant dfference n the level of nequalty n Ngera as the natonal trends measured by Gn coeffcent decrease from 0.43 n 1985 to 0.41 n 1992, due to the mpact of SAP and the postve growth rate of GDP durng the perod. Inequalty ncreased from 33

2 0.41 n 1992 to 0.49 n 2004, and declned from 0.49 n 2004 to 0.45 n 2010 due to the mpact of Natonal Economc Empowerment and Development Strategy (NEEDS) and other nsttutonal reforms that began n 2004 and the sustaned growth rate recorded durng ths perod. Smlarly, among the geo-poltcal zones the trend shows a declne n the natonal average n 2004 due to the mpact of economc reforms. There s also a dsparty n educatonal attanments n whch there s low rates n the North-East, North-West and North-Central zones wth lteracy rates of 50.6, 53.8, and 59.6 percent respectvely. On the other hand, lteracy rates n the South-West, South-South and South-East zones are much hgher at 78.6, 82.6 and 79.3 percent respectvely (UNDP, 2009). Ths study therefore, analyzed nequalty on rural households n North-East Ngera wth the am to acheve the attanment of two objectves, frst s to provde a descrptve analyss of households soco-economc characterstcs as t relates to ther ncome, and second s to examne factors of nequalty n the study area. 2. Lterature revew Poverty s multfaceted. Poverty manfests tself n dfferent forms dependng on the nature and extent of human deprvaton (FOS, 1999). Poverty s assocated wth the ndvdual or famly nadequate access to resources for a decent standard of lvng (e.g., ncome and consumpton, housng, health, clean water and santaton, nutrton, productve potental, and other central dmensons of well-beng). The World Development Report (1990) refers to poverty as the nablty to attan a mnmum lvng standard. Inequalty, on the other hand, mples the dsperson of a dstrbuton whether ncome, consumpton or some other welfare ndcators or attrbutes. Income nequalty s often studed as part of the broad analyss coverng poverty and welfare. Thus, nequalty s a broader concept than poverty because t s defned over a whole dstrbuton (Ltchfed, 1999). Followng the work of Kuznets (1955, 1966), on the relatonshp between development and ncome nequalty, many development economsts have been nspred to fnd the major sources of ncome nequalty. In ths regard, Datt and Ravallon (1992), proposed a method that decomposed poverty change nto ncome redstrbuton, ncome growth and a resdual component. Kakwan (1997) adopted an axomatc approach to decompose poverty change nto ther growth and redstrbuton components. Baye (2005), used Shapley (1953) value for assgnng enttlement n dstrbutve analyss and assessed the wthn and between sector contrbutons to changes n poverty levels n Cameroon n 1984 and It was found that the wthn sector effect dsproportonately accounted for ncrease n poverty, but the between sector contrbutons n both rural and sem urban areas ncrease poverty. Smlarly, Oyekale et al., (2006) used the 2004 Natonal Lvng Standard Survey (NLSS) data to determne poverty n Ngera. The result showed that the overall Gn ndex for Ngera was and n sectoral sense ncome nequalty was found to be hgher n rural areas wth Gn ndex of as compared to urban areas whch s ). They however concluded that employment ncome ncreases ncome nequalty whle agrcultural ncome decreases t. However, Awoyem and Adeot (2004), found that agrcultural ncome s nequalty ncreasng whle wage and self-employed ncome are nequalty decreasng. Oluwatayo (2008), used Lorenz curve and Gn coeffcents to analyse ncome nequalty and welfare status of rural households n Ekt State. Hs fndngs suggest that there s an unequal dstrbuton n ncome and other ndcators of welfare wth a Gn coeffcent of Therefore the study of ncome nequalty becomes relevant to economc development because hgh level of ncome nequalty produces unfavourable envronment for growth and development. 2.1 Inequalty measures Inequalty refers to dspartes n ncome dstrbuton n a populaton. Inequalty could also be estmated for other welfare ndcators than ncome, for example non ncome dmensons such as nequaltes n educaton, employment, health etc. Further nequalty gves a broader perspectve snce t ncludes the entre populaton nstead of only people lvng below a poverty lne (World Bank, 2005). It s commonly measured by the Gn coeffcent whch can be derved from the Lorenz curve. The Lorenz curve shows the cumulatve proporton of ncome n relaton to the cumulatve proporton of a populaton. The Gn s gven by the area between the Lorenz curve and the 45 lne of equty from orgn. The Gn vares between 0(total equalty) and 1 (complete nequalty). A value of 0.55 and above s a hgh level of nequalty, s mddle-hgh, s mddle and 0.35 and below s a low level of nequalty (Bourgugnon, 2004). The Gn coeffcent of nequalty s gven by 1 G = n n n = 1 j = 1 2 2y y y j Where the y s mean ncome, n s the total number of ndvduals, y are ndvdual ncomes (Ltchfeld, 1999). The Gn satsfes several mportant propertes of measurng nequalty. It also satsfes the Pgou-Dalton crteron of transfer senstvty,.e. an ncome transfer from rch to poor reduces nequalty. However the Gn (1) 34

3 cannot be broken down to compare subgroups or sources of ncome snce the sum of the Gn n subgroups s not equal to the total Gn of the socety (World Bank, 2005). Another method of measurng nequalty s regresson based decomposton method. It uses regresson technque to model the per capta ncome or expendture as a functon of explanatory varables. Ths determnes how much ncome nequalty s accounted for by each explanatory varable and how much s unexplaned, as measured by the error term. The decomposton s done by specfyng an ncome functon as shown below: Υ = Χβ + ε (2) Y s per capta ncome or expendture, X s the matrx of explanatory varables, ε s the stochastc error term. The explanatory varables are exogenous ndvdual, household characterstc whch determnes ncome level. They capture household s ncome generatng capacty n both formal and nformal labour markets and selfemployment. These nclude, educaton, occupaton of head, assets, market and locaton varables. Snce the econometrc results yeld estmates of the ncome flows attrbuted to household varables, they allow the decomposton of nequalty by factor ncome. The ncome contrbuted by the socoeconomc varables as gven n the estmated regresson equaton s: = =1 k k Υ y (3) k The ncome flow can then be used drectly to calculate decomposton component for all regresson varables and the contrbuton of each of the soco-economc factors (X) to Gn nequalty can be estmated. 3. Study area The study area s North-East zone of Ngera, whch comprses of about one fourth of the countres land mass. Its stuates wthn 9-14 N and 8-15 E (Iloeje, 1976). Poltcally, the zone comprses of Bornu, Yobe, Adamawa, Taraba, Gombe, and Bauch (sx States). Most of these states share boundares wth nternatonal communtes lke Cameroun, and Chad Republcs. It experences acute dryness on the sol, whch hardly supports luxurant growth of grass and other flora bodverstes. However, there s luxurant growth of trees around rverbeds, mountans and hghlands, whch supports arable and anmal husbandry. The regon s populaton s made up of both sedentary arable farmers and mgratory herdsmen, manly of Fulan ethnc group. There are about 200 ethnc groups n ths zone, among whch are the Tv, Fulan, Bachama, Kutep, Jukun, etc (TEE-REX, 2003). Ths zone was chosen because t has hgh prevalence of poverty and ncome nequalty (NBS, 2006) 3.1 Methodology The survey data used n ths study was collected by Ngera s Natonal Bureau of Statstcs (NBS) formerly known as the Federal Offce of Statstcs (FOS). They were based on Harmonzed Natonal Lvng Standard Survey (HNLSS, 2010) data of households that was carred out n November 2009 to October It covers 36 states and Abuja. It comprses a large sample sze of 34,769 usable households. A total of 4,999 were used for the analyss, whch s the total populaton of North East zone of Ngera. 3.2 Model specfcaton Analytcal Technques The analytcal methods nclude; descrptve statstcs, Gn ndex and the Tobt regresson model. Descrptve Statstcs: - Descrptve statstcs (such as means, tables, frequences, percentages) were used to analyze, summarze and descrbe the socoeconomc characterstcs of the respondents. Tobt Regresson Model: - Tobt model was employed to ascertan the determnants of poverty and nequalty among households n the study area. The Tobt model (Greene, 2003) employed was of the form; Where: Y= Dependent varable X = Vector of explanatory varables β = Vector of respectve parameters l = Independently dstrbuted error term Υ = β Χ + l (4) Thus, the explanatory varables n the regresson analyss were and measured as; X1 = Age (n years) X2 = Gender (Female = 1, Male = 0) X3 = Martal status (Marred = 1, Sngle, Dvorced or Wdowed = 0) X4 = Household sze X5 = Years of formal educaton j 35

4 X6 = Income/ expendtures of respondents (Nara) 4. Results and dscusson 4.1 Soco-economc characterstcs of respondents A soco-economc profle of the respondents (Table 1) shows that 94.8% of respondents were male wth an average household sze of 5. The larger the sze the larger the resources requred to meet basc needs of food and other necesstes. The soco-economc profle also revealed that the majorty (67.9%) of the respondents had no formal educaton. Poverty s concentrated among persons wth no educaton and those wth only prmary educaton. Ths has serous mplcatons on the poverty level because educaton plays an mportant role n creatng awareness n farmng communtes and educated people are better equpped to source nformaton. Majorty of the respondents are marred. Table 1: Dstrbuton of respondents based on ther soco-economc characterstcs Varable Frequency Percentage Age (years): Educatonal qualfcaton: No formal educaton Prmary educaton Secondary Post secondary Others Household sze Sex Male Female Martal Status Marred Dvorced 101 Wdowed Source: Author s computaton from NLSS (2009) data 4.2 Gn decomposton analyss The decomposton of nequalty components by average per capta expendtures across the North-East zone s presented n tables below Inequalty decomposton wthn the sector Table 2 shows the decomposton of nequalty wthn urban and rural sector n the North Eastern part of the country. The rural regon shows the hghest level of nequalty wth Gn ndex of 0.94, though rural areas are characterzed by low productvty of labour and unequal dstrbuton of ncome and factors of producton. Inequalty n urban areas s not as hgh as n rural areas, though t s stll hgh consderng the value of Gn that ndcate 0.55 and above as hgh. An ndcaton of 0.72 shows that ncome has ncreased relatvely more but the dstrbutonal effect has not favoured the urban poor. Also, more than 80% of the nequalty n the zone s accounted for wthn the groups whle less than 10% of the nequalty s accounted for the dfferences n urban and rural locatons n the zone. 36

5 Table 2: Inequalty Decomposton by Resdental Locaton of the Household Head Group Gn ndex Populaton share Income contrbuton Absolute contrbuton Relatve contrbuton urban rural Wthn group Between grp overlap total Source: Author s computaton from NLSS (2009) data Inequalty decomposton by educaton of the household head The level of nequalty has been known wth ncrease n educatonal attanment n any socety, the hgher the ncome, the hgher the nequalty. Inequalty s hghest wthn the household where 97% s accounted for secondary school, followed by the prmary educaton (95%) as measured by the Gn ndex. Majorty of them had secondary educaton. Ths s an ndcaton that the north eastern part of the country stll lag behnd n terms of educaton whch s the socal equalzaton ladder. Wthn the group decomposton shows that 35% account for wthn the group and 23% between the groups, ts contrbuton to total poverty s very low. Therefore, dfferences n educatonal level by the household head are ndcatons that ther ncome level dffers dependng on ther job. Table 3: Inequalty Decomposton by Educaton of the Household Head Group Gn ndex Populaton share Income Absolute Relatve None Prmary educaton Secondary educaton Post secondary College degree Wthn group Between Overlap(resdue) Total Source: Author s computaton from NLSS (2009) data Inequalty decomposton by gender of household sze However, nequalty ndex s smlar no matter the gender of the household head as the Gn ndex for both sexes s 93.3 and 95.9 respectvely. Ths s shown n the decomposton analyss as revealed by Table 4; t shows that nequalty s hgh n both sexes. Table 4: Inequalty Decomposton by Gender of Household Sze Group Gn ndex Populaton share Income Absolute Relatve Male Female Wthn group Between Overlap(resdue) Total Source: Author s computaton from NLSS (2009) data Determnants of poverty and nequalty n North-East Ngera Tobt regresson model was used to determne the poverty and nequalty status among the rural farmng households n North East, Ngera. The lkelhood rato statstcs as ndcated by χ2 statstcs ( ) was hghly sgnfcant (P < ), suggestng the model has a strong explanatory power. The results of the analyss as shown n Table 5 revealed that age, martal status, household sze, and educatonal level are the major determnants of nequalty n the study area. The coeffcents of age and gender were postve whch mples that ncrease n the value of any of these varables may lkely ncrease the probablty of beng poor. As the respondents are gettng older, the lkelhood of beng poor s ncreasng. Ths s can be justfed based on the fact that elderly persons declne n strength and productvty as they get older as well as declnng health condtons. Household sze also ncreases the lkelhood of beng poor and ths could be because of ncrease n household sze drectly or ndrectly reduces ncome per head (per capta ncome) as well as mpar standard of lvng of the households. Educaton s another determnant of nequalty n the study area; human captal theory suggests postve 37

6 correlaton between educatonal level and job opportuntes and capacty to earn hgh ncome. Hence, employment opportuntes tend to vary between ndvduals dependng on the level of educatonal attanment. Ths s because one s labour productvty s affected by the amount of knowledge, nformaton and sklls acqured and educaton can be a major determnant of nequalty. Table 5: Tobt estmaton result of determnants of nequalty n North-East Ngera zyz coeffcents t-value Household sze *** sex Age *** Martal status *** Level of educaton *** *10%, **5%, ***1% level of sg Pseudo R 2 = Number of obs = Log lkelhood = Prob > ch2 = Concluson and recommendatons. Increasng ncome nequalty and poverty contnue to be the most challengng economc problem facng most of the developng countres today. Ths study examnes the determnants of nequalty n North-East Ngera. The soco-economc profle of the respondents shows that 94% were male wth an average household sze of 5 members and majorty of them were marred. 67.9% of them had no formal educaton. The result of the Gn ndex(0.94) shows that the rural areas has the hghest level of nequalty, nequalty s also hgh wthn the household sze where 97% accounted for secondary school educaton, ths shows that majorty had secondary educaton. The tobt result shows that age, sex, educaton and household sze are major determnants of nequalty n the study area. Therefore t s recommended that: There s need to promote human captal development through vocatonal and techncal educaton n order to enhance self employment, wealth creaton and poverty reducton. Western educaton should be promoted and encouraged through senstzaton programmes and campagns on varous socal meda as educaton s the socal equalzaton ladder, most especally n North-East Ngera. There s need n creatng awareness on famly plannng to control brth. The household sze n North- East Ngera s farly large and ths has serous mplcatons on the ncome and well beng of households. References. Adeot, A.I. and Oyekale, T.O. (2006): Measurement and Sources of Income Inequalty among Rural and Urban Households n Ngera. PMMA Workng Paper , Poverty and Economc Polcy Research Network ( Awoyem, T.T and A.I. Adeot, (2004) The Decomposton of Inequalty by Sources of Income: The Rural Ngeran Experence. Afrcan Journal of Economc Polcy,11 (1):1-16 Agbokhan, B. (1997) Poverty Allevaton n Ngera: Some Macroeconomc Issues. Ngeran Economc Socety's Annual Conference Proceedngs, Ibadan. Atknson, A.B and Bourgugnon, F. (2000) Introducton: Income Dstrbuton and Economcs. Handbook for Income Dstrbuton, Vol. 1, North Holland, Amsterdam. Baye, P.M. (2005) Structure of sectoral Decomposton of Aggregate Poverty changes n Cameroon. Paper presented at The Internatonal Conference on Shared Growth n Afrca, Accra, Ghana (July) Brdsall, N. (2005) Rsng Inequalty n the New Global Economy. World Insttute for Development Economc Research (WIDER) Angle. No 2. PP. 1. Bourgugnon, F, M. Fourner, and Gurgand M,(1998) Dstrbuton, development and educaton :Tawan, World Bank, Mmeo. Central Bank of Ngera (2009): Annual Report and Statement of Accounts, pp. v Commsson for Afrca (CFA) (2005) Our Common Interest. Report of the Commsson for Afrca. Webste -w.w.w.commssonforafrca.org. pp. 219 Dalt, G. and Ravatlon, M. (1992) Growth and Redstrbuton Component of Changes n Poverty Measures: A decomposton wth Applcaton to Brazl and Inda n the 1980s. Journal of Development Economcs. Federal Offce of Statstcs (FOS) (1999) Poverty profle for Ngera, Lagos, pp. 24. Igbatayo, S.A. and Igbnedon, S.O. (2006) The Challenges of Poverty Reducton n Sub-Saharan Afrca: The South Afrcan Experence. Journal of Busness Admnstraton and Management. Vol.1,Number 1, 38

7 Duncan Scence Journals, pp.35 Kakwan, N. (1997) On Measurng Growth and Inequalty Components of Changes n Poverty wth JORIND 9(2) December, ISSN Applcaton to Thaland. A dscusson paper at The Unversty of New South Wales. Kuznets, S. (1955) Economc Growth and Income Inequalty. Amercan Economc Revew 45 (March). pp Kuznets, S. (1966) Modern Economc Growth. New Haven, Conn; Yale Unversty Press Ltchfeld, J.A. (1999) Inequalty Methods and Tools. Text for World Bank's ste on Inequalty, Poverty and Soco- economc performance. Okoje, C.E.E, Anyanwu, J.C., Ogwumke, P.O. and Alayande B.A. (2000) Poverty n Ngera: An Analyss of Gender Issues, Access to Socal Servces and the Labour Markets. A draft Report to the Afrcan Economc Research Consortum (AERC), Narob, Kenya. Oluwatayo, I.B. (2008) Explanng nequalty and welfare status of households n rural Ngera: Evdence from Ekt State. Humannty and Socal Scence Journal 3 (1): 70-80, 2008 Sala-Martn, X, and Subramanan, A. (2003) Addressng the Natural Resource Curse: An Illustraton from Ngera. May. PP. 1. Shapley L. (1953) A Value for n-person Games, In: H.M. Kuhm and A.W. Turker (eds.) Contrbuton to The Theory of Games. Vol. 2.Prnceton Unversty Press. Soludo C.C. (2006) Can Ngera Be The Chna of Afrca? Beng a Lecture Delvered at the Founder's Day of the Unversty of Benn, Ngera; Nov. 23. pp. 10. Unted Natons Development Programme (UNDP) (1990) Human Development Report: New York. Oxford Unversty Press. World Bank (1996) Takng Acton to Reduce Poverty on Sub- Saharan Afrca: An Overvew. Washngton, D.C. pp World Bank (1996) Ngera's Poverty n the mdst of Plenty: The Challenges of Growth wth ncluson. Populaton and Human Resource Dvson, West Afrca Department, Afrca regon. World Bank (2000) Attackng Poverty: World Development Report, 2000/2001. Washngton D.C. pp109 39

8 The IISTE s a poneer n the Open-Access hostng servce and academc event management. The am of the frm s Acceleratng Global Knowledge Sharng. More nformaton about the frm can be found on the homepage: CALL FOR JOURNAL PAPERS There are more than 30 peer-revewed academc journals hosted under the hostng platform. Prospectve authors of journals can fnd the submsson nstructon on the followng page: All the journals artcles are avalable onlne to the readers all over the world wthout fnancal, legal, or techncal barrers other than those nseparable from ganng access to the nternet tself. Paper verson of the journals s also avalable upon request of readers and authors. MORE RESOURCES Book publcaton nformaton: Academc conference: IISTE Knowledge Sharng Partners EBSCO, Index Coperncus, Ulrch's Perodcals Drectory, JournalTOCS, PKP Open Archves Harvester, Belefeld Academc Search Engne, Elektronsche Zetschrftenbblothek EZB, Open J-Gate, OCLC WorldCat, Unverse Dgtal Lbrary, NewJour, Google Scholar