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Proceedings of 2nd International Conference and Doctoral Colloquium organized by Faculty of Social and Management
Sciences, Bayero University, Kano-Nigeria pp. 327-336, 2018
327
Microeconomic Analysis of the Determinants of Urban Poverty in Katsina
Metropolis, Nigeria
Usman Musa Adamu
School of Management, Katsina State Institute Technology & Management.
Katsina-Nigeria
Email: adamu.usman@ksitm.edu.ng
&
Nura Aliyu Kabuga
Department of Economics,
Bayero University, Kano-Nigeria
Corresponding Author`s email: nakabuga.eco@buk.edu.ng
Abstract
This paper contributes to the large body of empirical literature on urban poverty using both logistic and Probit regression models. The main
objective is to conduct microeconomic analysis ofthe major factors that may likely influence the probability of urban household being ofpoor
or out of poverty line in Katsina Metropolis, Nigeria. Using a sample of about 220 households, the results suggest, dependency ratio, poor
electricity supply, proportion of average expenditure on food are some of the significant factors that probably influence or increase level of
poverty, while years of schooling, gainful employment, monthly expenditure, and access to credit significantly decrease poverty among
urban households. The paper concludes reducing urban poverty in Katsina Metropolis implies implementing policies that promote
employment opportunities, increase spending on public infrastructure especially in educational and health sector, as well as adequate
access to affordable credit facilities. Given the findings of this paper, it is recommended that that infrastructural facilities like electricity
supply, good roads, health care and effective educational systems should be improved upon for the benefit of poor. The government should
carry out more vigorous reform in the financial market with a view to bringing it to establish a special financial institution solely
responsible for giving soft loans to the poor. Thus, credit facilities should be made available and accessible to the poor. To further bridge
the income gap between the rich and the poor, the policies that will ensure income transfer should be strengthen. In addition, government
should introduce policy reform and incentive system to encourage labour shift from services to manufacturing sector.
Key words: Urban Poverty, Katsina Metropolis, Nigeria, Logit and Probit models.
Introduction
One of the most discussed issues in the contemporary literature of urban or development economics especially in developing and emerging
economies isthe dynamics associated with urban poverty. This interest is particularly heightening by observed evidence that suggests
majority of the global population are increasingly becoming urban people. In 2012, United Nations (UN) warned that the rate population
growth in urban areas is likely going to double unless effective measures are taken to tackle the incidence. They further provided evidence
that shows the percentage of urban population has increased from 43% from 1990 to 25% in 2011, and this is expected to grow to about 67%
by 2050. This means as growth of urban areas continue at an accelerated pace and share of poor in the urban areas continue to increase
greatly, poverty is likely going to be more pronounced in the urban areas. This is particularly so if the rapid nature of urban growthis such
that the capacity of local authorities to deliver services and infrastructure to people is been undermined, and as observed by Duque et al.
(2013) this tendency can increase urban poverty and intra-urban inequalities.
Although, a lot of studies has been conducted on urban poverty, many have argued that the dynamics of urban poverty is mostly
underestimated (Akinbode, 2013; Akerele et al., 2012). This is probably because urban poverty is generally found to be multidimensional in
nature, and most of the approaches developed to measure it have been unsatisfactory, and this explain growing incident of poverty and
worsening inequality in many economies around the world. The concern over growing poverty is one of the major reasons, Nigeria has been
formulating policies and programs aimed to improving welfare of its population. However, despite several efforts at reducing high rate of
poverty and widening gap of inequality, World Bank (2014) report suggests, poverty is still significant at 33.1% in Nigeria. This is despite
its massive resources and growing population to support productive activities, and plenty of natural resources including oil. Recently, World
Bank (2017) reported also that at least 35 million more Nigerians were living in extreme poverty in 2013 compared to in 1990. In this report,
extreme poverty is defined as living on less than $1.90 a day.
Proceedings of 2nd International Conference and Doctoral Colloquium organized by Faculty of Social and Management
Sciences, Bayero University, Kano-Nigeria pp. 327-336, 2018
328
The underlying motivation for this paper is the reported evidence that suggest tackling issues that influence urban poverty is increasingly
becoming an important issue in the policy agenda of many developing countries. Even as studies have shown that poverty is widespread
around the world, it is obvious from reported evidence in the literature that it is more pronounced in developing countries where it is found
both in the rural and urban areas. Incidence of poverty is much higher in the rural areas but its impact in the urban areas is nonetheless
considerable (Adamu and Kabuga, 2016; Kabuga and Adamu, 2015; Kabuga et al., 2015; Akerele et al., 2012). This paper contributes to the
large body of empirical literature on urban poverty using both logistic and Probit regression models. The main objective is to conduct
microeconomic analysis of the major factors that are likely to influence the probability of urban household moving out of a poverty line in
Katsina Metropolis, Nigeria.
The discussions in this paper are relevant to ongoing policy and scholarly debate on how to monitor poverty and its dynamics urban areas.
This paper is of the view that criticalanalysis of urban poverty levels as well as its associated socio-economic determinants are very
important from the perspective of policy implementation and economic wellingof the urban poor bearing in mind that both the incidence as
well as poverty depth would help re-orientate interventions on poverty and enhance the effectiveness of poverty reduction strategies in
Katsina Metropolis, Nigeria. The understanding in this context is that households possess some specific characteristics which can be skilfully
manipulated through policies to improve their welfare status. It is also argued in this paper that, understanding urban poverty presents a set
of issues distinct from general poverty analysis, and therefore requires sophisticated techniques. Although, there is no single approach in
investigating the dynamics of urban poverty, there are common good practice as observed by Akerele (2012) that facilitate the process
identifying and evaluating major determinants of urban poverty.
This paper is organized as follows; after this introduction, section 2 reviews some relevant empirical literatures. Section 3 presents
methodology employed for the data analysis. Section 4 reports empirical results and discussion. Finally, section 5 contains summary,
conclusion and recommendations.
Literature Review
There are number of studies conducted to investigate determinants of povertyin the economic literature. However, a review of most of these
studies suggests, the literature can be categorized into two strands. The first strand of the literature is conducted based on macro-economic
analysis, while the second on basis of micro-economic analysis. Recent studies for the macroeconomic analysis include the works of Nyasha
et al. (2017), Yusuf and Sumner (2017), Liddle (2017), Kolwole et al., (2015), Olofin et al., (2014), and Ravallion, (2001). Most of these
studies have used time series data to investigate determinants of poverty in each economy or economies.
However, unlike in macroeconomic analysis that is basically used time series, and is generally is seen as top-down approach, the interest of
this paper is on microeconomic analysis, a type of bottoms-up approach that allow us to focus largely on household as a unit of analysis.
Several studies have used this micro level of analysis to investigate determinants of poverty across developing countries. This include a
study conducted by Kabuga and Adamu (2015) where logit regression is used to find evidence that suggests some of the major factors
associated with poverty status of the households in rural areas of Katsina State include age of the head of household, gender of the
respondents, household size, non-farm jobs, and years of schooling, while level of household income, asset ownership, dwelling unit type are
reported to be insignificant in explaining probability of being poor.
Furthermore, Akinbode (2013) using Ordinary Least Square (OLS) as a technique of analysis also revealed that educational level of heads,
household size, gender of heads, dependency ratio and access to credit exerted significant effect on household welfare. Akerele et al., (2012)
found evidence to suggest dependency ration, household assets and educational status of the household head are some of the major factors
influencing poverty in Ekiti State, Nigeria. Similarly using logit regression model, Okurut et al. (2008) found evidence to suggest household
size, education, income, age among others were the major determinants or causes of poverty in Uganda. Ibrahim and Umar (2008) reveals
the major determinants of poverty in Nassarawa State, Nigeria include household size, number of income sources of the household head,
number of household members employed outside agriculture and the number of literate adult males and females in the household.
Proceedings of 2nd International Conference and Doctoral Colloquium organized by Faculty of Social and Management
Sciences, Bayero University, Kano-Nigeria pp. 327-336, 2018
329
Using the probit model, Olaniyan et al (2005) determine the probability of being poor as a result of a unit change in a variable. The results
indicate that education reduces the probability of being poor in a household. Judging from the marginal effects, the largest impact is for those
who have up to a post-secondary education, which is followed by those with a primary education. The Human capital has a decreasing effect
on the probability of being poor among all rural households, whether they are engaged in farm activities or non-farm activities. Ghazouani
and Goaied (2001) used logistics and probit model to find that main factors that determine poverty in Tunisai are head of family’s level of
education, dependency ratio, and socio-professional category of the head, family residence and region
It is therefore obvious while reviewing the literature that limited number of studies has been conducted in relation to poverty in Katsina
State. This concern provides the necessary motivation for conducting this study and therefore adds to scant literature on urban poverty in
Nigeria and Katsina which could be used to guide scholarly debate and policy discourse.
Methodology
Study Area
Katsina Metropolis is in the northwestern part of Nigeria. It has a population of 318,132 people (census, 2006). The people of the area are
mostly farmers. They are also involved in trade and public service. The metropolis is located some 160 miles east of the city of Sokoto, and
84 miles northwest of Kano, close to the border with Niger. The ancient city is the centre of an agricultural region producing groundnuts;
cotton, hides, millet, and guinea corn and have mills for producing peanut oil and steel. Generally, climate varies considerably according to
months and season. The two climates are: a cool dry season from December to February; a hot dry season from March to May; a warm wet
season from June to September; a less marked season after rains during the months of October to November, characterized bydecreasing
rainfall and a gradual lowering of temperature.
Sampling Technique and Sample Size
The paper employed a simple random sampling technique to select the sampled respondents for the study at least 10 areas on the study area.
They are Tundun Yaleda, Tudun Matawalle, Kofar Sauri, Korongida, Rafukka, Sabon Gida, Darma, Makudawa, Kofar Durbi, and Tundun
Kasira. The third stage of the sampling process was the random selection of rural households from each of three rural communities. Finally,
a sample of 20 respondents were taken from each of area selected to ensure proportionate representative of the entire population. The process
yielded a total sample of 200 household in the selected 10 areas. However, the minimum sample size obtained was inflated by 20% to make
the sampled respondents large enough as to not only take care of non-response, incomplete responses and refusals but to increase the
likelihood of statistically significant result. The total sample size used for this study was 220 respondents. The data generated for this study
was collected using of questionnaire.
Model Specification
The paper started by using Foster, Greer and Thorbecke (FGT) poverty index as wasdeveloped by Foster et al. (1984) to analyze poverty in
the study area. The index is widely used as a class of additively decomposable measure of poverty. Themeasure presumes the headcount
index, as well as the poverty gap; and allows for the distributional sensitive measure of poverty through the choice of a poverty aversion
parameter “u”; the larger the value of the “u”, the greater the weight given by the index to the severity of poverty
The general specification of the model is given below:
=
−
=m
i
i
iZyZ
N
P1
,1
(1)
Proceedings of 2nd International Conference and Doctoral Colloquium organized by Faculty of Social and Management
Sciences, Bayero University, Kano-Nigeria pp. 327-336, 2018
330
Pθ= FGT index (0 <Pθ< 1).
N = Total number of the sampled households for the study
Z = Poverty line ($2 is equivalent to N 724 Nigerian currency).
yi = The per capita expenditure of ith household.
θ = FGT parameter (θ ≥ 0)
m = individual household whose per capita adult expenditure is below poverty line.
In this context, following Akerele (2012), the “θ” takes on a value of 0, 1, 2, with different interpretations. Therefore, when θ = 0, it indicates
poverty incidence (the headcount ratio) which is a measure of the proportion of household living below the poverty line. However, when θ =
1, it measures the intensity of poverty or poverty gap. It is an indication of how far a household is from the poverty line. It provides
information on the proportion of the poverty threshold (line) which an average poor household would require in addition to what it has at
present to escape poverty. Lastly, if θ = 2, it measures the severity of poverty. It gives more weight to the poorest of the population. The
closer the value is to 1, the higher the seriousness of poverty.
Given effort in this paper to assess severity of poverty in study area using the severity index that provides information on additional financial
resources (as a proportion of the poverty line) that would be required to lift the core poor out of severity of poverty, the paper also used some
econometric models to assess the determinants of poverty in the study area. First of all, of all, the paper started with a specification of a
dynamic model of the probability of being poor as follows:
itiititit uxpp ++
+= −
1
*
(2)
where the subscript i = 1, . . ., N indexes households; the subscript t = 2, . . ., T indexes time periods; p*it is a latent dependent variable for
being in poverty; xit is a vector of explanatory variables; ηi is a term capturing unobserved household-specific random effects; uit is a random
error term assumed to be normally distributed, and γ and β are parameters to be estimated. The observed binary outcome variable is
Pit = {1 if >0, and 0 otherwise.
On the basis of this assumption, following Kabuga and Badamasi (2014), this paper analyses the determinants of urban poverty in the in
Katsina Metropolis using binary outcome models, namely logit and probit regression models. The logic behind a class of binary response
models arise from the following set of relationships:
)(}{}0{}0*{}1{
iiiiiii xFxPxPyPyP =−=+===
(2)
Where P is a function taking on values strictly between zero and one, that is, 0<P(z)<1, for all real number z. In addition, the expression
implies evaluating the probability of xiβ i.e. F(xiβ) depends on the distribution function of εi. If the logit model follows logistic distribution
function, the following simple model is obtained:
i
i
x
x
ii e
e
xLxF +
== 1
)()(
(4)
On the other hand, for the probit model the standard normal distribution function is,
Proceedings of 2nd International Conference and Doctoral Colloquium organized by Faculty of Social and Management
Sciences, Bayero University, Kano-Nigeria pp. 327-336, 2018
331
dttxxF i
x
ii
−== −
2
2
1
exp
2
1
)()(
(5)
Thus, both logit and probit can be derived from an underlying latent variable model. Let y* be an unobserved or latent, variable, determined
by
ii
xy
+=*
, where y = 1 [y*>0] (6)
The function 1[.] is called the indicator function, which takes on the value one if event in brackets is true, and zero otherwise. Therefore, y is
one if y*>0, and y is zero if y*≤0. While ε is independent of x and that ε has either the standard logistic distribution or standard normal
distribution. In either case ε is symmetrically distributed to about zero. In other words, both logit and probit models are very similar; the only
difference is the distribution of the error term (ε). Thus, when both models are applied not much differences is observed as both models give
almost similar result (Gujarati, 2007). For the purpose of this study, the binary choice model is specified in form of.
ijii XY
++=
0
*
(7)
Following the studies conducted by Kabuga and Badamasi (2014), Kabuga and Adamu (215) and Adamu and Kabuga (2016), the logit and
probit model is used to analyze the factors influencing poverty in Katsina Metropolis, Nigeria is given as follows:
ii xxxxxxxxxY
++++++++++= 9988776655443322110
(8)
Table 1: Variables Measurement
Variables
Types
Description
Yi
Binary
The probability of household is poor takes the value of 1,if otherwise, 0.
x1
Continuous
Years of Schooling
x2
Continuous
Monthly expenditure
x3
Continuous
Dependency ratio
x4
Categorical
Access to credit
x5
Categorical
Employment
x6
Categorical
Type of dwelling unit
x7
Continuous
Expenditure on Food
x8
Continuous
Expenditure on Health
X9
Categorical
Poor electricity supply
Sources: Computed by authors
Results and Discussion
The paper started by revealing the descriptive statistics of the major characteristics of the households as reported in table 2. From the result
the average age of the sample respondents is 45.23, implying that the respondents are in their middle age, and probably at the peak of their
productivity. The result also shows that most the respondents have at least twelve years of schooling which can significantly influence their
future wellbeing. With average of five (5) dependence and six (6) household size, the respondent’s ability to escape out poverty could be
constrained especially if most of them relied on head of the household for survival. The respondents’ average expenditure on food is 14,520
Naira, which based on current living expenses is to meagre. Their expenditure on health is about 4,330 Naira.
Table 2: Descriptive Statistics of Respondents` Socioeconomic Characteristics
Variables
Mean
Standard Deviation
Age in years
45.23
8.61
Years of schooling
12.45
5.32
Dependency ratio
5.44
2.54
Proceedings of 2nd International Conference and Doctoral Colloquium organized by Faculty of Social and Management
Sciences, Bayero University, Kano-Nigeria pp. 327-336, 2018
332
Household size
6.31
4.16
Household expenditure on food in Naira (Monthly)
14520.25
15.94
Household expenditure on healthin Naira (Monthly)
4330.41
8.25
Sources: Field Work, (2017)
The finding of this study also reported in table 3 revealed a poverty incidence index value of 0.51. This can be interpreted to mean at 51% of
the sampled households were poor. The result also reported a poverty depth value of 0.39. This finding can be interpreted to mean an
average poor household in the area has to mobilize financial resources in excess to about 39% of US $2 (N 724), which is their poverty line
in order to survive or escape poverty. The severity of this poverty is also reported by severity index value of about 0.27. This an average
poor household in the area required at least 25% of the US $2 (N 724) per day in addition to what is already in the possession of the
household in order to escape from severe poverty. It is important to note that the closer the value of the index is to 1, the more severe the
poverty status of the respondents.
Table 3: FGT Index
FGT Index
Sample Value
Incidence (P0)
0.51
Depth (P1)
0.39
Severity (P2)
0.27
Sources: Computing from the Field Work using Stata, (2017)
The poverty profiles of the respondents based on their socio-economic characteristics are reported in Table 4. The result suggests the three
indices of poverty shown were more pronounced among female headed households compared to male. This is evidence with finding
suggesting poverty incidence (headcount ratio) value of about 0.41 for females as against 0.34 for male. The result can be interpreted to
mean households headed by female are more vulnerable to poverty than their male counterpart.
Looking at the result it is also evident that household heads with larger household size as well as higher dependency ratio appears to be more
susceptible to poverty than those with fewer household size or dependency ratio. For example, incidence, gap and severity of poverty are
more visible with more than nine (9) household size (0.91, 0.73, and 0.64 for incidence, depth and severity respectively) compared to those
with 1 to 3 household size. The situation is even much worse for those with dependence that do not contribute to household income and
economic wellbeing. The result also indicates that more educated household heads have smaller poverty incidence, depth and severity
compared with other households. This is a clear testimony of the fact that poverty reduce with education.
Table 4: – The Poverty Profile of the Households based on Socioeconomic Characteristics
Socioeconomic Characteristics
Poverty incidence
(P0)
Poverty Depth (Gap)
(P1)
Severity of Poverty
(P2)
Gender of Household
Male
0.34
0.43
0.23
Female
0.41
0.49
0.31
Household Size
1-3
0.31
0.41
0.21
4-6
0.39
0.45
0.32
7-9
0.64
0.71
0.59
9-above
0.91
0.73
0.64
Dependency ratio
No dependence
0.09
0.22
0.07
1-3
0.29
0.37
014
4-6
0.54
0.53
0.49
7-9
0.73
0.82
0.61
9 - above
0.98
0.86
0.69
Education
Primary Education
0.74
0.45
0.21
Junior Secondary School
0.61
0.26
0.20
Senior Secondary School
0.31
0.22
0.16
Tertiary Education
0.15
0.19
0.14
Source: Researchers’ computation from survey data (2017).
Proceedings of 2nd International Conference and Doctoral Colloquium organized by Faculty of Social and Management
Sciences, Bayero University, Kano-Nigeria pp. 327-336, 2018
333
Based on the main objective of this paper, the major factors that may likely to influence poverty in the study area is analysed. The result was
however, obtained using Stata 14 software. The result of the estimation is presented on Table 5 and 6. In this context, logit and probit models
were used to analyse dichotomous dependent variable which takes the value 1 or 0. Therefore, in the model the dependent variable (Y) is the
binary that is taken the probability value of 1 if the household is poor, if otherwise, 0. At the same vein, the marginal effect for the models is
also shown Table 6.
Table 5: – The Result of Logit and Probit Models
Variables
Logit
Marginal Effect
Probit
Marginal Effect
Years of Schooling
-0.645***
(0.081)
0.021
(0.010)
-0.319***
(0.099)
0.072
(0.023)
Monthly income
-0.344***
(0.132)
0.219
(0.019)
-0.299**8
(0.114)
0.096
(0.034)
Dependency ratio
0.814**
(0.125)
0.088
(0.035)
1.360***
(0.235)
0.069
(0.022)
Access to credit
-0.295***
(0.115)
0.052
(0.026)
-0.382**
(0.171)
0.243
(0.091)
Employment
-0.354***
(0.173)
0.085
(0.036)
-0.115***
(0.033)
0.095
(0.040)
Type of dwelling unit
0.063
(0.076)
0.009
(0.821)
0.750
(0.542)
0.084
(0.633)
Expenditure on Food
0.230**
(0.095)
0.117
(0.041)
0.172***
(0.091)
0.226
(0.083)
Expenditure on Health
0.148***
(0.033)
0.055
(0.008)
0.993***
0.210
0.076
(0.033
Poor electricity supply
0.062**
(0.031)
0.035
(0.013)
0.366***
(0.190)
0.117
(0.059)
Constant
0.943**
(0.355)
0.424**
(0.089)
No of Obs. = 220
LR ch2 (6) = 34.31
Prob>chi2 = 0.0006
Pseudo R2 = -13.359
No of Obs. = 220
LR ch2 (6) = 37.40
Prob>chi2 = 0.0010
Pseudo R2 = -14.363
Source: Researchers’ computation from survey data (2017) using STATA 14. Also to note figures in parenthesis are standard errors. The
asterisks are p-values implies ***significant at 1%, **significant at 5% and *significant at 10%.
The results in Table 5 suggests dependency ratio, poor electricity supply, expenditure on food, and expenditure on health are some of the
significant factors that probably influence or increase level of poverty, while years of schooling, gainful employment, monthly income, and
access to credit significantly decrease poverty among urban households.
Looking at the true picture of the result, the finding suggests that years spent by the head of the household in schooling is more likely to
decrease the probability of the households being poor. After controlling for all other variables, the finding portrays the likelihood that, as
households’ years of schooling by at least one year, they are more likely to improve their wellbeing by approximately 0.02% when marginal
effect for logit model is used and 0.7% when marginal effect for probit model is used instead.
The result also reveals an inverse relationship between household`s monthly income is negative and probability or likelihood of being poor.
This implies that those without any sources of generating income are more likely to remain poor compared to those who have sources of
income. Thus, the marginal effect suggests that 1% increase in the income of the respondent would probability decrease possibility of
remaining poor by at least 0.2% when marginal effect for logit model is used by 0.1%when marginal effect for probit model.
The result also suggests direct relationship between the number of dependent and probability of escaping out poverty. This means as the
number of dependent increases, the probability remaining poor increase by at least 0.8% when marginal effect for logit model is used by
1.4% when marginal effect for probit model. Furthermore, the result in Table 5 also reported that, the access to credit is inversely related to
probability of remaining poor. The finding also implies that after holding all other factors constant, households that are gainful employed are
more to escape out of poverty than those without work. This means households strongly relied on a gainful employment to lessen the effect
of poverty among them. The results also reported that household expenditure on food can positivelydetermine likelihood of the household
becoming poor. The result suggests a likelihood that as household expenditure on food increases by at least 1%, the probably of probability
Proceedings of 2nd International Conference and Doctoral Colloquium organized by Faculty of Social and Management
Sciences, Bayero University, Kano-Nigeria pp. 327-336, 2018
334
of remaining poor increases by at 0.1% when marginal effect for logit model is used by 0.2%when marginal effect for probit model. Almost
the same result was found for expenditure on health.the likelihood of remaining poor also increase when household is confronting with poor
electricity supply.
Conclusion and Recommendations
This paper used microeconomic analysis to examine the major factors that may likely influence the probability of urban household poverty
status in Katsina Metropolis, Nigeria. The paper has shown using a sample of about 220 households that dependency ratio, poor electricity
supply, proportion of average expenditure on food are some of the significant factors that probably influence or increase level of poverty,
while years of schooling, gainful employment, monthly expenditure, and access to credit significantly decrease poverty among urban
households.
The finding of the paper also reported that a poverty incidence 0.51. This can be interpreted to mean at 51% of the sampled households were
poor. The result also reported a poverty depth value of 0.39. Thus, an average poor household in the area has to mobilize financial resources
in excess to about 39% of US $2 (N 724), a figure regarded in this paper as their poverty line in order to survive or escape poverty. The
severity of this poverty is also reported by severity index value of about 0.27, which means an average poor household in the area required at
least 27% of the US $2 (N 724) per day in addition to what is already in the possession of the household in order to escape from severe
poverty. It is important to note that the closer the value of the index is to 1, the more severe the poverty status of the respondents.
The paper concludes by suggesting that reducing urban poverty in Katsina Metropolis implies implementing policies that promote
employment opportunities, increase spending on public infrastructure especially in educational and health sector, as well as adequate access
to affordable credit facilities. Given the findings of this paper, it is recommended that that infrastructural facilities like electricity supply,
good roads, health care and effective educational systems should be improved upon for the benefit of poor. The government should carry out
more vigorous reform in the financial market with a view to bringing it to establish a special financial institution solely responsible for
giving soft loans to the poor. Credit facilities should be made available and accessible to the poor. In order to further bridge the income gap
between the rich and the poor, the policies that will ensure income transfer should be strengthen. In addition, government should introduce
policy reform and incentive system to encourage labour shift from services to manufacturing sector.
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