Chapter Three Correct

7/30/2019 Chapter Three Correct

1/12

Principal component and factors analysis will be used to summarize each multidimensional

variables (household head characteristics, socio-economics characteristic, biophysical factors,

household income factors, expenditures factors, and multidimensional poverty indicators) this

process give a standardize score or weight for each, which are usually referred to as the

composite variables or latent variables. The process is a statistical technique commonly used to

build a predictive or descriptive model of group discrimination, based on priory existence of

distinct class or group in the variables, the model classify each observation into one of the

group.Sabine,L. and Brian S. E., 2004; Peter T., 1997; Koustsoyiannis, 2001 all claimed that it is

an approach of summarizing and uncovering any patterns in a set of multivariate data, essentially

by reducing the complexity of the data and providing a factor from the unclassified variables

which has a greater power of contribution on the relationship. The technique also served as

means of investigating whether a number of variables of interest are linearly related to a smaller

number of unobservable factors commonly known as the latent factors or composite variables.

( Sabine, L. 2004, Alkire, S and Maria, E., 2010; Foster, J.E. 2007; Joseph, F,. William, C.B.,

Barry, J.B., and Rolph, E.A., 2010). This weighted scores, composite variables or latent variables

are used examine the interrelationship between the variables, setting the rest as independent

while multidimensional poverty factors are set as dependent variable whose effect are

determined by the action of the independent. This process was carried out because

multidimensional poverty relationship is complex and interdependent hence cannot be separatedinto dependent and independent variables straight away( Joseph, F,. William, C.B., Barry, J.B.,

and Rolph, E.A., 2010; Anonymous, 2011; Wilkinson D.J. 2012; Asselin, L.M and Tuan, A.V

2009; Rencher, A.C 2002; Abdeljaouuad, E and Paolo, V 2012; Heikon, C., Justinia, R and

Robert, D, 2011;Neil,H.T 2002, Rodrigo,P 2011 Hardle,W. and Simar L, 2003 ) but with this

analysis the complexity will be broken and hence allow for categorization between a dependent

and independent variables and can be used for further analysis.

3.8 Conceptual framework

Indeed, Poverty has various sign including lack of income and productive resources sufficient to

ensure sustainable livelihood; hunger and malnutrition, ill-health limited or lack of access to

education and other basic services; increased morbidity and mortality from illness; homelessness

1


2/12

and in adequate housing; unsafe environments; and social discrimination and exclusion which is

characterized by lack of participation in decision making and civil, social and cultural life. The

traditional method of measuring poverty were basically using continuous interval scales on

income or expenditure and set a poverty line, however this method has not been comprehensive

in providing a clear understanding of the poverty.

Perhaps, this set the need to understand the relationship between the actual multidimensional

poverty and the variables used to measure poverty previously (such as Income and expenditure),

however these multidirectional variables require the use of categorical and ordinal variables to

capture and measure all the aspects consider to reflect the poverty. However, these variables are

numerous which need to prune but an objective process of pruning is to be undertaking using

statistical approach to extract the hidden variable of Poverty, Income and expenditure which can

be used for the assessment of relationship. The use of such statistical tool to reduce the response

variables is because of wide inter-correlation within the variable which if allowed will affect the

estimation and basically make the standard error of estimates to be badly biased and hence result

in the breaking down of the assumption of multiple linear regression so by this practice it serves

in solving the problem of multi-collinearity observed within variable and maintaining the

variability of the response. Schematically the description of the variables based on research

objectives is as follows:

2


3/12

Model for Analysis of Interdependent relationship among multidimensional poverty determinants of Farmers in Gombe North

3.0 METHODOLOGY

3.1 Study Area

The study will be carried out in five local government area of Gombe state (Gombe north

senatorial zone) the survey is to be conducted include Dukku, Funakaye, Gombe, Kwami, and

Nafada. The study area has a land mass of d six hundred and fifty five square kilometres

(8655km2), has a total population figure of six hundred and seventeen thousand eight hundred

and four (617,804)people out of which are four hundred and eighty thousand five hundred and

eighty four (480584; 77.8%) males with two hundred and nine thousand two hundred and twenty

(209220; 22.2%) females. Gombe north was bounded by Darazau (Bauchi state) from the north,

Kumo and Akko village all in Akko L.G.A. (Gombe central) from the south and west

respectively, by Kwadon in Yamaltu-Deba L.G.A. (Gombe central), North east by Ngalda in

Potiskum, LGA (Yobe state). There are twenty eight thousand two hundred (28,200) households

3


4/12

in the 5 local government areas to be surveyed with an average household size of 11 people,

nearly half of which are females.

Gombe north lies at altitude 700 207m above sea level has a maximum temperature 40.80C

and mean temperature 15.30C with the coldest months between December and February. The

hottest months ranges from March to May with temperature of up to 33.60C, the mean

temperature fall up to 180C with the coldest month from November to February (Baba D. 1999).

Gombe has unimodal rainfall distribution with total annual rainfall of 1528mm and mean annual

rainfall of 109mm with total annual rainy days of a 129 days rain and a mean 9 days per month.

The rainfall spread between the months of April to October the local government is characterized

by savannah grass land some woody trees of height ranging from 2m to 3.5m. The dominants

trees include Khaya (Madaci), neam (dogonyaro), parkia trees(Locust bean). The soil is

predominated by sandy to sandy loam with a pH ranging from 6.0 7.5 low in Nitrogen, with a

C.E.C (cations exchange capacity) ranging between 21 35%. The major crops grown are maize,

millet, cowpea, cotton, groundnut, bambara nut and cassava, vegetables include onion, tomato,

pepper.

Rice production in the zone is not widespread. This is because of the rainfall situation and

absence of water reservoirs, only Nafada, Hashidu had the potential for rice production because

of the existence of river Gombe Abba and Hashidu(Hassan, M. 2011)

3.2 Sampling procedure and Sample Size

Multistage sampling method will be employed to sample three hundred farming households. At

the first stage, all local government in the study area will be selected, they include: Dukku,

Funakaye, Gombe, Kwami and Nafada. The second stage involves the random selection of threefarming communities (villages) from each local government area. The third stage sampling

involves proportionate random sampling selection of twenty farming household from each

community (village). A total of three hundred farming households will constitute the sample size

for the study. The sampling plan is presented in table 1.

4


5/12

3.3 Sample frame

Table 1: The sampling plan for the study

LGA Community

Distance

from LGA

Distance from

State(Km)

Estimated Male

Population

Estimated Female

Population

Total

Population

Number of

Household

Dukku

Dukku 0 73 18879 9782 28661 2697

Gombe Abba 19.12 85.39 2934 1487 4421 267

Hashidu 29.9 87.65 3313 1716 5029 331

Jarkum 14.51 64.25 3541 1716 5257 393

Malala 44.54 92.45 11223 5550 16773 1603

Zange 28.74 51.33 1397 808 2205 93

Funakaye

Ashaka Gari 9.45 78.01 4622 2339 6961 385

Bage 12.34 78.93 2051 1063 3114 205

Bajoga 0 71.12 35964 18738 54702 2398

Ribadu 32.4 45.44 1710 864 2574 132

Wawa 34.28 57.54 5231 25247755 265

WuroBapparu 14.58 76.37 751 362 1113 50

Gombe

Bajoga 0 3.5 NA NA NA NA

Shamaki 2.6 2.6 55942 29551 85493 3729

Dawaki 2.36 2.36 20343 10746 31089 1356

Bolari 2.16 2.16 50854 26863 77717 3390

Fantami 1.5 1.5 30514 16119 46633 2034

Jekadafari 1.9 1.9 35600 18805 54405 1978

London

Maidoruwa 3.2 3.2 20343 10746 31089 1565

Kumbiya-

Kumbiya 3.5 1.5 NA NA NA NA

Ajiya 3.5 1.5 25428 13432 38860 1496

Kwami

Kwami 49.5 13.53 7112 2401 9513 790

Malam Sidi 0 25.92 4226 2255 6481 352

Bojude 58.7 32.65 8819 2868 11687 802

Tappi 53 26.68 4843 1696 6539 484

Doho 7.9 19.14 3339 1629 4968 304

Dukul 22 43.84 4906 2439 7345 491

Nafada

BarwoWunde 8.14 91.65 2798 1424 4222 233

Nafada 0 97.88 13512 6784 20296 901

Jigawa 33.56 126.36 3966 1829 5795 406

BarwoNasarawa 10.44 92.11 12038 6501 18539 926

Birin Bolawa 25.03 70.52 6290 3154 9444 419

Birin Fulani 24.87 71.48 6095 3029 9124 406

Total 480584 209220 617804 30881

Source: Hassan M (2011)

5


6/12

3.7 Method of Data Collection

The study will obtain data majorly from primary source. Structured questionnaire will be used as

an instrument for data collection on household level. The questions will feature socioeconomics

and demographic information of the households, household head characteristics, poverty pointers

and associated features, households perceptions on the causes of poverty; households

information on income, expenditure and some biophysical features of farmers farms.

3.9 Analytical Technique and Model Specification

Combinations of analytical techniques will be employed for data analysis to achieve the

objectives of this study. These will include descriptive which include mean, mode median as

well as inferential statistics using multivariate technique which include cluster analysis, principal

component analysis, multidimensional scaling, stepwise regression, discriminant, factor analysis,

conjoint analysis and correspondence analysis.

3.4.1 Descriptive and Inferential Statistics

Descriptive statistics such as mean, standard deviation, frequency distribution will be used to

describe the data and is used to achieve objective 1, while

responses on the socio economics and demographic characteristics, households perceptions on

the causes of land degradation;land use pattern and management. This will be used to achieve

objective 1, 2 and 8 as well as part of objectives 4 and 6. Cross tabulation will be use to

disaggregate the variable by household land use type, State and community type.

Inferential statistics in the form of Chi-square and t test will be used to test the statistical

significance of the relationships or differences between variables as well as the goodness of fit of

the distribution. More specifically, Chi-square test will be used to test hypotheses 1 and 2.

Stepwise regression will be done on the collected data. The first step will use principal

component analysis, factor analysis and discriminant to trim the number of variables (The one

that fitted most will be taken).

3.9.2 Measurement of Composite (Latent) variables

6


7/12

It is common practice in socioeconomics studies to compute latent or composite variables on

parameters like socio-economic factors, quality assurance, these parameters are usually gotten

from several observed indicators (response items) each requiring responses in dichotonomous,

polychotonomous (likert type), ordered categories. Traditionally product moment correlation is

used in such composite scores or weighted variables, through their additive indices of these

indicators without regard to either measurement and distributional properties of the constituents

indicators or their relative contribution to the composite score. While, the Composite score so far

gotten are then treated as continuous variables in compilation in general linear model technique,

which assume that such variables are measured without error.

Consequently, this approach leads to at least two major problems when trying to model

relationship among composite scores or when comparing, their magnitude and weighed additives

are ignored, in that some indicators contribute more to measurement of the composite than others

the second it may invalidates the composite score if one or more of the indicators measure a

construct other than the one under consideration. Hence this new insight is helpful in minimizing

the problem so far encountered in the traditional method.(Ken, R. 2006), Karl, G.J. and Irini, M.

(2001)

The basic idea behind the analysis is that, given a set of ordinal or categorical response variable

X1,X2.Xp, after the analysis a latent factor z1,z2..zk fewer in number than the observed

variable are obtained but contain essentially the same information which is called a latent factor.

These latent factors are to account for the dependencies among the response variables, in the

sense that if the factor is held fixed the observed variable would be independent. The latent

function involves the determination of linear equation like regression that will predict which

group the case belongs to.

Latent function can express as below;

LFj= Li1X1 + Li2X2++LipXp + a

Where the LFj = Latent factor function, Li=Latent co-efficient or weight for that variable, X=

respondents score for that variable a= constant, i=number of predictor variables. The function is

similar to regression but its co-efficient are un-standardize co-efficient analogous to the co-

7


8/12

efficient in regression. These co-efficient maximized the distance between the criterion

(dependent) variable. Standardize latent co-efficient can also be used like beta in weighted

regression. The variable shown below are all inserted into the analytical technique and analysed

the co-efficient obtained from the analysis are latent factors grouped into the categories shown

and were used for further analysis as dependent and independent variables for the second

regression for the assessment of relationship and produce a standardized co-efficient.

8


9/12

Variables Definition

Latent (composite) variables

Poverty

=Y

Socio Economics

= a1

Household Head Characteristics

= a2

Household Expenditure

= a3

Household Inco

= a4Original variables obtained from survey to be transformed to latent variables

ccessibility &

aterial Lack

xclusion

ulnerability

hysical weakness

ender equality

nvironmental con.

ousing and

othing

=X1

=X2

=X3

=X4

=X5

=X6

=X7

Gender

Age

Marital Status

Family type

Occupation

Religion

Farming Experience

Farm size

Membership of

Co-operative

Category

= X8

= X9

= X10

= X11

= X12

= X13

= X14

=X15

=X16

=X17

Household Size

Number of Children below

18

Number of Others above 18

-60

Number of Elderly above 65

Number of Internal

Migration

Location

Number of years in Co-

operative

Education Level

= X18

= X19

=X20

= X21

= X22

= X23

=X24

=X25

=X26

=X27

Total food expenses =X28 Quantity of

Farm OutputPurchase of Tools

and Animals

=X29

Housing expenses =X30 Total nonfarm

received

Education expenses =X31 Off-farm

receivedTransportation/Co

mmunication

=X32

Repairs and

maintenance

=X33 Total value of

Farm assets

Social contribution =X34 Equipment

Mechanical

Total expenses on

farm

=X36 Tools non

mechanical

EquipmentLoans repayments =X37

9


10/12

If the factors are independent, it follows that the correlation between and is

From the above equation 1 is a suitable representation of the latent factors in the model if

response variable are continuous variables measured on an interval or ratio scale. However, it

cannot be used if the response variables are ordinal or nominal. In those cases, it is obvious

that the probability of each response pattern as a function of

Hence it becomes

expressed

as

...(4a)

Or

..(4b)

Where responses

ectors of

measurement errors

10


11/12

Ken, R.(2006) ; Karl,G.J(2001)

3.10 Linear regression model

This model will be used to achieve objective ii of this study. The model is based on the multiple

linear regressions. The function is therefore expressed as:

.. (4)

Where:

= + Xi

= Poverty Score

= Socioeconomics factors, Household expenditure, Household Income, Household

Head characteristics,

= constant term

= regression coefficient

= disturbance term or error term

3.11 Farm budgeting technique:

Household budget as the detailed physical and financial expenses between certain period. Net

household income (NHI) as the difference between the Gross Income (GI) and total expenses

(TE) Total expenditures food + Total non-food expenditure + social expenses + tax

&depreciation (N). (Olukosi and Erhabor, 2005) this technique will be used to compute for total

expenses and is presented with the following equation:

11


12/12

Net Household Income = GI THE TSE

THE + TSE = TE

Therefore NHI = GI THE - TSE

Where:

NHI = Net Household Income (N)

GI = Gross Income(Farm + Non farm) (N)

THE=Total household expenditure (N)

TSE = Total social expenditure (N)

TE = Total expenditures food+ Total non-food expenditure + social expenses + tax

&depreciation (N)

3.12 Descriptive Statistics

Descriptive statistics will also be used in this study. It involves the use of frequency, percentages,

etc

12

Date post:	14-Apr-2018
Category:	Documents
Upload:	aishatu-musa-abba
View:	219 times
Download:	0 times

Chapter Three Correct

Documents