At 2017 OWSD-BIU International Conference Determinants of food Poverty Dynamics among Rural Households in South-South Nigeria Presentation At 2017 OWSD-BIU International Conference By   John Chiwuzulum Odozi Edo University Iyamho

Outlines Introduction/ Objectives Theoretical/conceptual framework/ Methodology Results Findings

WHAT DO WE KNOW OF THE POVERTY PROBLEM IN NIGERIA? Increasing since the 1980s with variation within and across states Being a public issue, resources allocated through programmes Dynamics that are social, economic, political and environmental remain daily reality for individuals and households

National Bureau of Statistics

Per Capital Expenditure Poverty Incidence by States 2004 2010 Bayelsa 40.00 44.00 Cross River 67.00 60.40 Delta 70.60 53.80 Edo 53.60 64.10 Rivers 56.70 47.20 Akwa Ibom 56.80 51.00 National 64.2 62.6 Source: NBS, 2015

FREE FOOD/MAIZE DISTRIBUTION GOVERNMENT LOAN FOR UNIVERSITY AND SAFETY NET PROGRAMMES FOR POVERTY REDUCTION AND FOOD SECURITY FREE FOOD/MAIZE DISTRIBUTION GOVERNMENT LOAN FOR UNIVERSITY AND   FOOD/CASH-FOR-WORK PROGRAMME (E.G. DIRECT CASH TRANSFERS FROM GOVERNM INPUTS-FOR-WORK PROGRAMME (FADAMA) DIRECT CASH TRANSFERS FROM DEVELOP SCHOOL FEEDING PROGRAMME LIVESTOCK TRANSFERS FROM NGOS TARGETED NUTRITION PROGRAMME FOR M GROWTH ENHANCEMENT SCHEME (GES) SUPPLEMENTARY FEEDING FOR MALNOURI SCHOLARSHIPS FOR TERTIARY EDUCATIO SCHOLARSHIPS FOR SECONDARY EDUCATI

Under this setting, poverty analysis that takes the dimension of time into consideration becomes relevant for policy makers. Hence the following questions becomes pertinent: What is the poverty exist and entry rate for South-South Nigeria What are the factors determining poverty transition

Theory: Household Intertemporal Living Standard model 𝑾= 𝟏 𝑻 𝒊=𝟏 𝑻 𝜷 𝒕 𝒖 𝑪 𝒕 𝜶 + 𝝆 𝑪 𝒕−𝟏 𝜶 +…+ 𝝆 𝒑 𝑪 𝒕−𝒑 𝜶 𝟏 𝜶 Where 𝒄 𝒕 = consumption in period t 𝑾 = living standard 𝑻 = Number of time periods P = Number of lags associated with the effects of past consumption 𝝆=parameter describing the strength of the inertia link over one period 𝜶 =intertemporal substitution parameter for the living standard at each time period 𝜷 =subjective actualization parameter

In a pioneering contribution, Sen (1976) conceptualized the poverty measurement problem as involving two exercises: (i) the identification of the poor and (ii) aggregation of the characteristics of the poor into an overall indicator. Use of income or consumption or expenditure : A person is said to be poor if his income falls below the poverty line. On the aggregation issue

Permanent income indicator the squared poverty Gap ( 𝑃 2 ) across all individuals n is measured as P 2 = 𝑛 −1 𝑖=1 𝑛 1− 𝑦 𝑖 2

Theoretical/Conceptual framework Poverty concept Transient poverty is temporary. Persons experience in & out of poverty Chronic-poverty is persistent. Persons remain in poverty a long period of time (McKay and Lawson, 2002)

Analytical framework: Count approach Methodology Analytical framework: Count approach Identifies transient and chronic poor based on the number of times observed to be in poverty(Foster, 2009, Jalan and Ravallion,1998). Aggregate poverty function disaggregates into Transient and chronic poverty functions with the imposition of the following conditions (1) Additivity and (2) Convexity Squared poverty gap (SPG) is used to measure poverty because it penalizes inequality amongst the poor (Foster, et al., 1984). This is expressed as: 𝑝 𝑦 𝑖𝑡 = 1− 𝑦 𝑖𝑡 2 𝑖𝑓 𝑦 𝑖𝑡 <1 =0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

being poor in both years (P–P), escaping poverty (P–NP), 2/3 mean per capita consumption expenditure as the poverty line. The classification into poor and non-poor in the two years defines four dynamic states: being poor in both years (P–P), escaping poverty (P–NP), falling into poverty (NP–P) being non-poor in both years (NP– NP).

  𝑃 𝑖𝑗 = Pr 𝑦 𝑖 =𝑗 𝑥 𝑖 = exp⁡( 𝑥 𝑖 𝛽 𝑗 ) 𝑖=1 4 exp⁡( 𝑥 𝑖 𝛽 𝑗 ) , 𝑗=1,2,3,4 The model is used to posit that the probability of poverty dynamics is a linear function of the following socio economic factors .

Education level in years Age in years Household size Asset Value in ₦ Land size in hectares Dependency ratio Price Index Esusu savings Micro Finance Access Education level in years Christian headed Married Widowed Female gender State location

The model is estimated using pseudo-likelihood procedure used to generate the response probabilities for household i. All analysis done in Stata 11 package Data set was sourced from NBS

RESULTS Poverty rates 2010 2012 POOR(P) 406(79.13%) 408(80.31%) Aggregate poverty rates, 2010 – 2012, South South, Nigeria Poverty rates 2010 2012 POOR(P) 406(79.13%) 408(80.31%) NONPOOR (NP) 106(20.87%) 100(19.69%)

POOR NON POOR TOTAL 71.06 8.09 79.15 NONPOOR 9.25 11.61 20.86 80.31 19.70 100.01

Poverty states Freq % POOR TO POOR 351 71.06 POOR TO NONPOOR 41 8.09 Poverty movement , 2010 – 2012, South South, Nigeria Poverty states Freq % POOR TO POOR 351 71.06 POOR TO NONPOOR 41 8.09 NONPOOR TO POOR 47 9.25 NONPOOR TO NONPOOR 59 11.61 Total 498 100

Gender P-P P-N N-P N-N rates FEMALE 65.2 6.25 14.3 71.43 MALE 72.7 8.6 Poverty movement by Gender, 2010 – 2012, South South, Nigeria Gender P-P P-N N-P N-N rates FEMALE 65.2 6.25 14.3 71.43 MALE 72.7 8.6 7.8 10.9 81.31

States P-P P-N N-P N-N rates Akwa Ibom 84.4 2.08 8.33 5.21 86.5 Poverty movement by states , 2010 – 2012, South South, Nigeria States P-P P-N N-P N-N rates Akwa Ibom 84.4 2.08 8.33 5.21 86.5 Bayelsa 77.8 5.6 14.8 1.8 83.3 Cross Rivers 87.2 4.3 6.4 2.13 91.49 Delta 44.9 21.3 7.9 25.8 66.29 Edo 77.1 10.4 6.2 87.50 Rivers 62.2 6.3 11.8 19.7 68.50

Education level in years .0040 (1.46)* REGRESSION ANALYSIS , MARGINAL ESTIMATES   P-P P-N N-P N-N Marginal estimates Household size .0550 (5.11)* -.0293 (-3.65)* Asset Value in ₦ -1.21e-07 (3.80)* 4.48e-08 (2.45)* Food Price Index .1312 (1.53)* -.1952 (-2.47)* Micro Finance Access .0748 (1.95)* -.1038 (-3.09)* Education level in years .0040 (1.46)* Married .1045 (1.58)* -.0604 (-1.73)* Widowed .1121 (1.34)*

  P-P P-N N-P N-N Marginal estimates Akwa Ibom .2040 (3.24)* -.1429 (-2.66)* Bayelsa .5339 (7.05)* -.1910 (-3.15)* -.2255 (-3.50)* -.1173 (-3.16)* Cross Rivers .2337 (4.37)* -.1378 (-3.35)* -.0805 (-2.44)* Delta .1105 (2.16)* -.0823 (-1.82)* -.0427 (-1.55)* Edo .1762 (2.69)* . -.0786 (-2.25)* -.1568 (-3.08)* Number of observations 303 Wald chi2(54) = 283.31 Prob > chi2 = 0.0000 Pseudo R2 = 0.3628 Log pseudolikelihood = -124.16971

FINDINGS 8.09% escaped poverty 71.06% remained poor. 9% fell into poverty. This reflects inflow to outflow from poverty. By gender analysis, 14% of female headed households fell into poverty compared to 8% male headed while fewer female headed escaped poverty(6%) compared to male headed(8%). Even though more chronic poverty is noted for male headed( 73%) than female headed(65%). FINDINGS

Inflow and outflow poverty mechanism results. The following variables were significant (0.05 significance level) Food price index: change in Food price by ₦1 brought about 13% increase in the probability of remaining poor. Also reduced the probability of moving into poverty by 19%. Asset : reduced the probability of staying in poverty and increases the probability of remaining non poor. Location: Being a farmer in Akwa Ibom, Bayelsa, Cross Rivers and Edo States increased the probability of remaining poor by 20%, 53%, 23% and 18% respectively but reduces the probability of transient poverty by 14%, 19%, 14% , 8% and 7% for farmers in Akwa Ibom, Bayelsa, Cross Rivers , Delta and Edo States respectively. Widow headed households: Increases the probability of remaining poor by 11% Micro finance access: increases the probability by 7.4% of poverty escape Household size: mixed, could bring about stay in poverty and also being non poor over two seasons

THANK YOU