1. 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

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

3. 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

4. National Bureau of Statistics

5. 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

6. 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

8. 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

9. 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

10. 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

11. 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

12. 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)

13. 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

14. 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).

15. 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 .

16. 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

17. 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

18. 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%)

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

20. 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

21. 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

22. 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

23. 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)*

24. 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) =
Prob > chi2 =
Pseudo R2 =
Log pseudolikelihood =

25. 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

26. 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

27. THANK YOU