3.0
Methodology
3.1
Study Area:
The study was conducted in Ebonyi State, which is one
of the five states in South Eastern Nigeria.
The state lies between latitudes 7031’E and 8030’E
and longitudes 5040N and 6045 N. It has boundaries on the North with Benue
State, on the South with Abia, on the West with Enugu State and on the East
with Cross River. It has an area of 5933
square kilometres with a projected population of about 2.2 million people (NPC
2006).
The rainfall distribution pattern and the tropical
equatorial climate characterised by high rainfall, high temperature and
sunshine give rise to two distinct seasons – the rainy and the dry
seasons. Rainy season starts from April
to October, while dry season lasts from November to March.
Ebonyi State with large expanse of arable land, large
population and conducive environment is imbued with the resources to produce
enough food for her people and for export as well as raw materials for food
processing enterprises (Ebonyi State Blue Print Report, 2004). According to Ebonyi State Blue Print 2004, on
strategies for food processing, storage, preservation, marketing and
distribution in Ebonyi State, thirteen (13) crops are produced in significantly
large quantities, out of which Cassava, Rice, Yam, Groundnut, Oil Palm, Maize,
Mango, Pepper, Oranges and Leafy vegetables were found to be currently produced
at levels, which can sustain processing industries in some localities after the
fresh food market needs are satisfied.
The State therefore is essentially agrarian with
majority of the populace as small-scale farmers living in the rural areas, more
than fifty percent (50%) of which are women.
They rear poultry and small ruminants like sheep and goats. These farmers also engage in non-farm
activities like trading, food processing and marketing.
The State has thirteen (13) Local Government Areas and
is divided into three agricultural zones, North, Central and South Zones with
their headquarters at Abakaliki, Onueke and Afikpo respectively. Ebonyi North Zone comprises Abakaliki,
Ebonyi, Izzi and Ohaukwu Local Government Areas. Ebonyi Central has Ezza North, Ezza South,
Ikwo Ishielu Local Government Areas, while Ebonyi South is made of Afikpo
North, Afikpo South, Ivo, Ohaozara and Onicha Local Government Areas.
3.2 Sampling Techniques:
From the purpose of this study, operational definition
of micro and small-scale agro-processing enterprises include those enterprises
with labour size of less than 10 workers or total cost of not more than 1.5
million, including working capital but excluding the cost of land for the
micro-enterprise category while the small-scale enterprises have a labour size
of between 10 – 49 workers or a total cost of not below 1.5 million and not
more than 50 million.
The three micro/small agro-allied processing
enterprises for the study were Cassava, Oil Palm Fruit processing and Rice
milling enterprises. Three of the 13
Local Government Areas were purposively selected from each zone, making a total
of nine (9) Local Government Areas for the study. The choice of the Local Government Areas was
on the basis of predominance of the crops in such Local Government Areas. The nine Local Government Areas selected for
the study are Abakaliki, Izzi, Ohaukwu, Ikwo, Ezza North, Ishielu, Afikpo
South, Ivo and Ohaozara. It was also
based on the number of processors in each Local Government Areas as provided by
Ebonyi State Agricultural Development Programme. One thousand and thirty nine processors
(1039) Cassava, Rice and Oil palm fruit processors were identified in the
selected nine Local Government Areas (EBADEP 2002).
A Focus Group Discussion (FGD) was conducted to find
out the positive and/or negative externalities of the agro-processing
enterprise on the people living within the enterprise locale. The FGD was conducted on groups made up of
males and females who have lived in the place for up to 2 years including the
Entrepreneurs. Each group was made up of
6 residents.
Table 3.1: Production of Cassava, Rice and Oil Palm
Fruit in Ebonyi State according to L.G.As in metric Tonnes
Local Government Area
|
Cassava
|
Rice
|
Oil Palm Fruit
|
|
1.
|
Abakaliki
|
36842
|
10481
|
2500
|
2.
|
Ebonyi
|
36842
|
9446
|
200
|
3.
|
Izzi
|
57558
|
12194
|
250
|
4.
|
Ohaukwu
|
08055
|
17450
|
375
|
5.
|
Ezza North
|
70063
|
17964
|
500
|
6.
|
Ezza South
|
36002
|
9231
|
350
|
7.
|
Ikwo
|
29722
|
7621
|
1500
|
8.
|
Ishielu
|
31168
|
7992
|
2500
|
9.
|
Afikpo North
|
31212
|
9285
|
15000
|
10.
|
Afikpo South
|
34698
|
10179
|
25000
|
11.
|
Ivo
|
22507
|
5771
|
4500
|
12.
|
Ohaozara
|
22860
|
5861
|
5500
|
13.
|
Onicha
|
27767
|
7119
|
6450
|
Source EBSG Blue Print (2004)
Table 3.2: Number of identified processors according
to Local Government Areas
Local Government Area
|
Number of identified Processors
|
||
1.
|
Abakaliki
|
38
|
|
2.
|
Afikpo North
|
65
|
|
3.
|
Afikpo South
|
198
|
|
4.
|
Ebonyi
|
38
|
|
5.
|
Ezza North
|
121
|
|
6.
|
Ezza South
|
35
|
|
7.
|
Ikwo
|
74
|
|
8.
|
Ishielu
|
37
|
|
9.
|
Ivo
|
187
|
|
10.
|
Izzi
|
30
|
|
11.
|
Ohaozara
|
198
|
|
12.
|
Ohaukwu
|
146
|
|
13.
|
Onicha
|
77
|
|
Source EBADEP – Service Providers in Ebonyi State
(2002).
Five (5) communities were randomly selected from each
Local Government, making a total of forty-five (45) communities. In each community two processors for each of
the three agro-processing enterprises were randomly selected. Altogether, two hundred and seventy (270)
copies of questionnaire were administered as interview schedules to the
respondents but two hundred and sixty four (264) copies were properly
filled. Below is a table showing number
of processors, communities and Local Government selected for the study.
Table 3.3: Number of processors, communities and
Local Government selected for the study
L. G. A.
|
Communities
|
Number of Processors
|
|
1.
|
Abakaliki
|
Ebia Unuhu, Nkaliki, Nkwagu, Enyigba, Ndiegu
Okpoitumo
|
30
|
2.
|
Izzi
|
Agbaja, Igbeagu, Onuenyim, Ndezi, Ezza Inyimagu
|
30
|
3.
|
Ohaukwu
|
Amike, Ezzamgbo, Umuezeoka, Umuogudu Osha, Effium
|
30
|
4.
|
Ezza North
|
Umuoghara, Oriuzor, Umuezeokaha, Ogboji,
Amawula
|
30
|
5.
|
Ikwo
|
Ndufu Alike, Ndufu Echara, Igbudu, Inyimagu,
Ekpa-omaka
|
30
|
6.
|
Ishielu
|
Ntezi, Agba, Ohoffia, Ezillo, Nkalagu
|
30
|
7.
|
Afikpo South
|
Owutu, Oso, Amangwu Ufueseni, Okporojo
|
30
|
8.
|
Ivo
|
Mgbede Akaeze, Akaeze-Ukwu, Ogidi, Okue, Ihe
(Ishiagu)
|
30
|
9.
|
Ohaozara
|
Okposi-Ukwu, Mgbom-Echara, Amata Uburu, Umunaga
Uburu, Ugwulangwu
|
30
|
Source: Field
Data 2007
A structured questionnaire was administered to the
processors to extract information about their socio-economic characteristics,
availability, accessibility and functionality of infrastructure/amenities and
agencies. Questions asked aimed at
extracting information on institutions such as Financial, Agriculture, Lands,
Commerce/Industry, Transport, Judiciary etc.
The role of government institutions in micro/small
agro-allied processing enterprise development was noted through information
from the relevant Ministries.
Information was sought on the presence and number of water boreholes,
tap water schemes, number of schools, hospitals and health centres, roads,
electricity etc. The presence and role
of some poverty alleviation agencies like NAPEP, ADP and Community Based
Poverty Reduction Agency etc was ascertained.
3.3
Method of Data Collection:
A reconnaissance survey was carried out to
authenticate the list of existing functional processing enterprises, financial
institutions and other relevant institutions as earlier enumerated. The researcher sought the assistance of
EBADEP Staff (Enumerators and Extension Agents) posted to these areas. Where there was no EBADEP Staff, literate
local leaders and teachers were used to cover the area.
Primary data were used for the study, which came from
two sets of structured questionnaire, one was administered to the entrepreneurs
while the other was to financial institutions in the area.
3.4
Nature of Data Collected:
Based on the objectives of the research, the following
data were collected. Objective One and
Two: Cassava, rice and oil palm fruit processors were identified. The related institutions were noted and they
include formal, non formal, government and non government, rural and
urban. The socio-economic characteristics
of these micro/small agro-allied processing entrepreneurs were collected and
they include age, sex, size of family, years of schooling, type of enterprise,
length of years in processing, years spent in skill acquisition, loan obtained
and source, number of formal and informal credit organisation, distance from
formal credit institutions, working capital of enterprise, number of
organisations the entrepreneur belongs to, age of equipment, number of
workers/employees, value of enterprise excluding land.
Objective Three:
Determining the issues and institutions that influence
the establishment and development of agro-processing enterprises. The following data and information were
collected: availability, accessibility and functionality of infrastructure
(Road, water, electricity, telephones), hospitals and health centres, credit
facilities (formal and non formal) land, raw materials, schools and colleges,
functional cooperatives (age grade groups, town union organisation, social
clubs, churches, market organisation).
Taxes imposed by government (market, income, haulage) as they affect
establishment and development of enterprises, urban markets for input and
outputs, presence of facilitating agencies like Non – Governmental
Organisations (NGOs), trade unions and processing associations. Availability of equipment/tool repairing/servicing
outfits.
Objective Four:
Assessing gender issues that influence institutional
involvement in the development of agro-allied processing enterprises. The following were examined: financial
assistance to the genders both by formal and non formal credit institutions,
land ownership opportunities for farming and development of agro-allied
processing enterprises, literacy level of the gender and suitability of
processing equipment to female entrepreneurs (female friendly equipment)
services and incentives, government allocation of resources, political
emancipation and participation.
Objective Five:
Determining how institutional performance
affects/influences agro-allied processing enterprises development and poverty
alleviation. Improvement of the
following due to existence of micro/small agro-allied in the area was
noted: increase in income (wages and
self employment), raised agricultural productivity (input, seeds, fertilizer,
tools and machinery), improved family healthcare, family nutrition and
education, enhanced housing and re-investment in enterprises, enhanced
technological capability, existence of repairing workshops, improved market
network for sale of output, presence of subsidiary businesses – spare part
shops, petrol/diesel/engine oil selling depots.
Objective Six:
Identifying the constraints to institutional
performance in micro/small agro-allied processing enterprises development and
discussing the implications of the finding on poverty alleviation issues. Variables collected were on: Availability and accessibility of finance,
existence of infrastructure (water, road, electricity). Issues relating to education and skill
acquisition, availability of functional market and market information,
appropriateness of machinery and equipment, types and level of policy
implementation consistency, poverty indices of the entrepreneurs, level of
judicial efficiency and sustainability of enterprise.
3.5
Method of Data Analysis:
3.5.1 Use of Descriptive Statistics:
Data collected for the study were subjected to
analysis using descriptive statistical tools.
Frequency distribution tables, percentages, means and cross-tabulations
were used to analyse objective I to VI.
3.5.2 Multiple Regression Model:
Multiple Regression Model was used in objective II to
ascertain the influence of socio-economic characteristics of the processors on
the amount of credit obtained from financial institutions and level of
assistance from Government Agencies and Non Governmental Organisations. Three functional forms were tested and the
one with the best fit chosen. These are
ordinary linear function, semi-logarithmic and double logarithmic
functions. The model with the highest
coefficient of determination R2 and showed many statistical
significant variables was adopted following (Kmenta, 1971). The implicit form of the regression model
used is
Y = f (X1,
X2, X3 ………. X13 e)
Where dependent variables are:
Y1 = Amount of credit obtained (Naira) from both formal
and non-formal credit institutions.
Y2 =
Level of assistance received (in percentage) from Government Agencies and Non
Governmental Organisations (NGOs).
Independent variables are:
X1 = Income from enterprise (Naira)
X2 = Age of Entrepreneur (Yrs)
X3 = Gender of Entrepreneurs Dummy (M = 0,
F = 1)
X4 = Years of Schooling (Yrs)
X5 = Distance from Formal Credit
Organisation (Km)
X6 = Number of Informal Credit Organisation
X7 = Number of Organisations Entrepreneur
belongs to
X8 = Working Capital of Enterprise (Naira)
X9 = Length of years in business (Yrs)
X10 = Age of Equipment (Yrs)
X11 = Number of Workers
X12 = Years of Skill Acquisition (Yrs)
X13 = Value of assets of Enterprise
excluding land (Naira)
e = Error term.
Stepwise selection method was applied to the second
regression analysis because none of the functional forms proved a good
fit. According to Madukwe (2004),
stepwise selection method is used in instances when full set of independent
variables contain excess information about group differences or when some of
the variables are not very useful in discriminating among the groups. By sequentially selecting the “next best”
discriminator at each step, a reduced set of variables will be found, which is almost
as good as and sometimes better than full set.
Excluding some variables have different resultant effect on dependent
variable.
The a – priori expectation from the first regression
is that Income from enterprise should have a positive relationship with Amount
of credit obtained from financial institutions (Y,) because the higher the
income of the processors, the more the willingness of credit institutions to
extend credit.
i.
Age of
Entrepreneur should have a negative relationship since the older the processor,
the less efficient, he becomes, because processing is laborious. Credit institutions would be reluctant to
extend credit to an old person due to the fear of death at old age.
ii.
Years
in schooling is expected to have a positive relationship with credit
acquisition for enterprise development.
Number of years spent in school enhances one’s knowledge and commands
respect and confidence in accessing credit from either formal or non formal
financial institutions.
iii.
Distance
from formal credit organisation has an a priori assumption that the nearer the
banks to the processors, the more credit they can access and vice versa. Therefore it has a positive relationship
because banks are one of the sources of credit.
iv.
Number
of informal credit organisation should have positive relationship with amount
of credit obtained since one can access more credit if they were many informal
credit organisation.
v.
Number
of organisation Entrepreneurs belongs to is expected to have positive
relationship because one can get more credit in form of being a member of a
cooperative. Increased number would mean
increased credit acquisition.
vi.
Working
Capital of Enterprise has a positive relationship with credit acquisition
because the higher the working capital, the more credit worthy, the more the
willingness of credit institutions to extend credit.
vii.
Length
of years in business is expected to have positive relationship with credit
acquisition because practice makes perfect.
Consequently, the more years one is in processing, the more competent he
is and the more confidence credit institutions would have on him. Therefore the more eager they can extend
credit to him.
viii.
Age of
Equipment is expected to have negative relationship with credit acquisition
from financial institutions. The older
the equipment, the less interested credit institution would be to extend credit
due reduced performance of equipment.
ix.
Number
of workers. The more the number of
skilled workers in an enterprise, the higher the rating and the more willing
the credit institutions to extend credit because they are assumed to be more
business conscious than those with few unskilled family members operating on
subsistence level. It therefore should
have a positive relationship.
x.
Years
in skill acquisition has a positive a priori expectation because knowledge is
power. Credit institutions are more
disposed to extending credit to an expert than to a novice in any chosen
career.
xi.
Value
of assets of Enterprise has a positive relationship or influence on amount of
credit from credit institutions. The
higher the value, the more serious the entrepreneur is considered to be,
therefore the more willing credit institutions are in extending credit.
In the second regression, the dependent variable was
the level of assistance received (in percentage) from Government Agencies and
Non Governmental Organisation (NGOs).
Forms of assistance from agencies and NGOs included financial, Training
(Technical/Business), Equipment Fabrication, Equipment Repairs/Maintenance and
Provision of Business Land. The
identified assistances are in five categories as listed above. If an entrepreneur got one, two or three
assistances, it was measured as one, two or three out of five respectively
(1/5, 2/5, 3/5 etc) and multiplied by 100 to get the percentage.
The independent variables are the same as in the first
regression. The a priori assumptions of
the independent variables with the dependent variable are as follows:
i.
Income
from Enterprise: It is expected that the
higher the income, the more the assistance from government agencies or Non Governmental
Organisations because it is believed that the enterprise would be
sustainable. Therefore there is a
positive relationship between them.
ii.
Age of
Entrepreneur. The older the processor
the less willing assistance could be extended to the person due to the fear of
death which would result to the collapse of the enterprise. It therefore has negative relationship.
iii.
Gender
of Entrepreneur. Female entrepreneurs
are expected to get assistance from Government but due to their low educational
status, they are not aware of many government programmes and Non Governmental
Organisations that could be of benefit to them.
It is therefore assumed that being female entrepreneur will have a
negative relationship with assistance from Government and NGOs. Male entrepreneurs are supposed to have
positive relationship due to higher educational level than the female
processors.
iv.
Years
in schooling: The higher the number of
years in school, the more awareness one has about Government Agencies and NGOs
that can be of assistance. Therefore
there is a positive relationship.
v.
Distance
from Formal Credit Organisation: The
nearer a processor is to bank, the easier it becomes to get assistance. There is a positive relationship and a priori
assumption is that nearness to banks would increase ease of obtaining
assistance.
vi.
Number
of Informal Credit Organisations: The
more the informal credit organisations, the more the chances of getting credit,
and this will give an opportunity to benefit from these organisations. There is a positive relationship.
vii.
Number
of organisations entrepreneur belonged to:
The more the number, the higher the chances of getting assistance. Therefore there is a positive relationship.
viii.
Working
capital of Enterprise: The higher the
working capital, the more assistance that would be given. There is a positive relationship
ix.
Length
of Years in business: It is assumed that
the more the experience in processing, the more the confidence that would be
reposed in the processor. Therefore
there is a positive relationship.
x.
Age of
Equipment: The older the equipment, the
more problematic it becomes. A priori
expectation was that the older the equipment the less assistance that would be
given to the processor.
xi.
Number
of workers: Government and NGOs
encourage cooperative spirit in enterprises.
A priori expectation was that increased number of workers would mean
increased assistance.
xii.
Years
in skill acquisition: Increase number of
years in skill acquisition means higher expertise. Therefore there is a positive relationship.
xiii.
Value
of enterprise excluding land: The higher
the value of enterprise, the more sustainable it would be and the more
assistance to the entrepreneur.
3.5.3 Test of Hypotheses:
For in-depth analysis of objective II, t and F tests
were conducted for hypotheses 1A and 1B.
t – test was used to ascertain how the amount of credit obtained from
credit institutions and assistance from Government Agencies and Non
Governmental Organisations (NGOs) relate to the socio-economic characteristics
of the entrepreneurs. The individual
t-values were tested to establish the significance of the concerned variable
with amount of credit from formal and non formal credit organisations as well
as assistance received from Government Agencies and Non Governmental Organisations. F – test was used to establish the overall
relationship between the socio-economic characteristics of the entrepreneurs
with the amount of credit obtained and assistance received.
Inter-correlation matrix model: This was used for objective II. to investigate
how the selected socio-economic characteristics of the entrepreneurs are
interrelated with one another. Thirteen
identified socio-economic variables relating to credit obtained from formal and
informal credit organisations were inter-correlated in hypothesis 1A while the
same socio-economic variables relating to assistance received from Government
Agencies and Non Governmental Organisations were inter-correlated in hypothesis
1B. It should be noted that if the
inter-correlation matrix coefficient is less than 0.5, it is taken that there
exist a poor relationship between the two but if greater than or equal to 0.5,
there is strong relationship correlation of a variable with itself is 1. (Bhattacharyya and Johnson, 1977).
The variables include:
X1 = Income from Enterprise (Naira)
X2 = Age of Entrepreneur (Yrs)
X3 = Gender of Entrepreneurs Dummy (M = 0,
F = 1)
X4 = Years of Schooling (Yrs)
X5 = Distance from Formal Credit
Organisation (Km)
X6 = Number of Informal Credit Organisation
X7 = Number of Organisations Entrepreneur
belongs to
X8 = Working Capital of Enterprise (Naira)
X9 = Length of years in business (Yrs)
X10 = Age of Equipment (Yrs)
X11 = Number of Workers
X12 = Years of Skill Acquisition (Yrs)
X13 = Value of assets of Enterprise excluding
land (Naira)
Ugwuonah 2005, stated that non-existence of
collinearity is one of the basic assumptions in using regression model and that
high collinearity implies that the independent variables have strong
relationship with each other. A case of
high multicollinearity is a problem because it suggests that more than one
variable was used to represent a particular characteristics that the use of a
single variable should have effectively represented. It is synonymous with error of double
counting in accounting.
3.5.4 Use of Cross – Tabulation
Many cross-tabulations were done to establish
relationship between the three processing enterprises (Cassava, Rice and Oil
Palm Fruits) and some variables associated with the entrepreneurs. Cross-tabulation with chi – square (X2)
output was used to test hypothesis two (II) and three (III). Cross – tabulation with X2 output
was used to investigate sets of relationships or association among
variables. The cross tabulation is in
form of a two-way table as elucidated by Joliffe (1986). The statistics X2 is defined as;
X2 = Σ Σ(nij
– eij)2
I = 1
eij
j = 1
Based or (r – 1) (c – 1) degree of freedom, where the
summation extends over all cells in the 2 x m contingency table.
nij –
Indicates the observed frequency in the 1 – jth cell.
eij –
Indicates the expected frequency in the 1 – jth cell.
The reason for the two-way table is to investigate
whether a relationship changes, when values of other variables change.
3.5.5 Likert Scale Technique:
Likert scale analysis was used to analyse objective
three (III) in determining influence of institutions on the establishment and
development of agro-processing enterprises.
Likert analysis was also applied to analyse part of objective four (IV)
– effects of institutional facilities to business environment and poverty
reduction.
Likert is a tool used in making explicit decision on
factors associated with a particular observed phenomenon from other possible
factors or variables.
Likert formula Xs = a Σfn a
Where Xs = Mean score. Nr.
Σ
= Summation
n
= Likert numerical value
f
= Frequency of each response
pattern
Nr
= Number of respondents to each
response category.
Decision Rule:
In the 4 point and 5 point likert scale, 2.5 and 3.0 are taken as cut –
off mark respectively. Five point likert
scale was used for the study and 3.0 mean taken as the cut – off mark, (David,
2005).
3.5.6 Impact Assessment Model:
Impact assessment performance evaluation of the
micro/small enterprises for the past three years was achieved using one-way –
ANOVA Post Hoc multiple comparison with Scheffe Test. It was used to analyse objective five (V) and
to verify the level of significance of the processing enterprises and to
determine the enterprise with the most impact.
Impact Assessment performance of the three processing
enterprises on business and family, neighbourhood and self esteem of the
processors was carried out to verify the level of significance of the impact
after three years of operation. This was
used to measure physical achievement, income growth, financial efficiency and
livelihood improvement indices.
According to Hulme (2000), intended beneficiary impact assessment
focuses on the outcome at individual, household, enterprises and community
level. The commonly used outcome
indicators include:
-
Economic
Indicators: Educational status, access
to healthcare, family planning and nutritional levels etc.
-
Gender
Empowerment includes control over assets, involvement in household or community
decision making and social networks including political leadership.
One – Way – ANOVA – Procedure produces a One – Way –
Analysis of variance for a quantitative dependent variable by a single grouping
(independent) variable. It is used to
test the hypothesis that the means of the various groups are equal. It is usually used when there are two or more
groups. In addition to determining that
differences exist among the group means, it equally indicates, which means
differ (Adekoya et al, 2004). The Post
Hoc performs further test after general test of variance, it allows for a wide
option of test that determine, which of the groups in question are similar or
different in terms of means.
Ugwuonah (2005) further stated that the Scheffe Post
Hoc Test is the same as performing a t – test on two treatments and determines
which of the groups have significantly different means by rating the various
group means. Usually homogenous groups
are classified under the same subset.
Impact assessment of the three processing enterprises (Cassava, Rice,
Oil Palm Fruit) on Business and family, neighbourhood and self esteem of the
processors was carried out to verify the level of significance and to determine
the enterprise with the highest impact.
3.5.7 Use of Factor Analysis:
Factor Analysis is a procedure of examining the
possibility that a large number of items or variables have a smaller number of
factors explaining their interrelationship.
The aim of the method is to account for the co-variances of the observed
or manifested variables in terms of a small number of variables known as
factors, where the factors are un-observable variables or theoretical
concepts. Analysis of objective six (VI)
was done by the use of factor analysis to isolate the factors that act as
constraints to institutional performance in micro/small agro-processing
enterprises development in the State.
Kaiser (1958) developed a rule of thumb that variables
with coefficient of 0.50 or more have high loading and may be used in naming a
factor. The rule has been generally
applied (Ogunfiditimi, 1979; Alimba 1999).
Click on the related links below and read more.
We can keep you updated on this information, please Subscribe for Free by entering your email address in the space provided.
Do you like this article? Share this article
CONTACT US TODAY FOR OUR SERVICES