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Living Standard of Migrants:
A Study of Katakhali Pourusova in Rajshahi District,
Bangladesh
..........................................................................................................................
Dr. Md. Rafiqul
Islam
(1)
Ahmed Omar Faruk
(2)
Md. Golam Mostofa
(3)
Professor Dr. Md. Entazul
Huque
(4)
- Associate Professor
and Chairman
Department of Population Science & Human
Resource Development, Rajshahi University,
Bangladesh
E-mail: rafique_pops@yahoo.com
- Department of Population
Science & Human Resource Development,
Rajshahi University, Bangladesh
E-mail: Faruk.bwcci@gmail.com
- Assistant Professor
Department of Population Science & Human
Resource Development,
Rajshahi University, Bangladesh
- Chairman and Head
Department of Business Administration
Bangladesh University, Dhaka, Bangladesh
..........................................................................................................................
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ABSTRACT
The
purpose of the present study is to observe
the living standards of migrants of Katakhali
Pourusova in Rajshahi district. The sample
data was collected from 505 respondents
using direct interviews. This information
was procured by purposive sampling method.
In this study, a multiple linear regression
model was applied to study migrants. It
was observed that people migrate to certain
places due to economic reasons and migration
can alter the lifestyle of individuals and
families. People migrate to new places with
the hope of improving their social and economic
status.
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Bangladesh
lies in the north eastern part of South Asia
between 23034/ and 26038/ North latitudes and
88001/ and 92041/ East longitudes. The country
is bounded on the north and the west by India,
on the east by India and Myanmar and on the
south by the Bay of Bengal. It has a total area
of 147,570 sq km (56,977 sq miles) of which
8236 sq km is rivers and 1971 sq km is forested.
The population of the country has increased
over the years. According to the Population
Census 2001 (BBS, 2003), the total enumerated
population of the country stands at 123,851,120
of which 63,874,740 were males and 59,956,380
were females. Of the total population 28,605,200
live in urban areas and 95,245,920 live in rural
areas, and thus the percentage of urban and
rural population is23.1% and 76.9% respectively.
Bangladesh
is a poverty stricken and agrarian based country.
Due to increasing poverty and landlessness as
well as underemployment and unemployment, Bangladesh
rapidly goes through deteriorating pconditions.
In such a situation, a large number of people
seek overseas employment especially, internal
migration has occurred to unlock the opportunity
of employment status. Migration is a form of
geographical or spatial mobility involving a
change of usual residence between clearly defined
geographical units according to United Nations.
Migration occurs due to the perception of spatial
differentials of opportunities - the idea that
different geographical locations offer different
level of potential well being to various sections
of the human population.
Migration
is a relatively permanent moving away of a collective
group, called migrants, from one geographical
location to another proceeded by decision making
on the basis of hierarchical order (Mangalam,
1968). Zelinsky (1971) said, "migration
is a physical and social transaction, not just
an unequivocal biological event". The study
of population migration has been a rapidly developing
branch of several academic disciplines. Economists,
sociologists, historians, psychologists, demographers
and geographers all find the residential movements
of the human population to be of importance
to their respective subjects and for this reason
the study of migration is both a multidisciplinary
as well as an inter-disciplinary field (White
and Woods, 1980).
There
are two main types of migration, internal and
international. Internal migration is an integral
part of the development process. It is influenced
by development (such as the building of roads,
economic activities and employment opportunities
in certain areas) and it influences development
(destination areas gain in skills and capital
while areas of origin lose out) (Chandra and
Chandra, 1998:60). There are relationships between
and among migration, urbanization and socio-economic
development. According to Skeldon (1992), "there
is a clear relationship between economic development
variables. The most developed countries have
the highest levels of urbanization and they
have low fertility and low rates of infant mortality.
The least developed countries, however, have
low levels of urbanization"
Migration
reflects people's responses to many different
factors such as social and economic inequalities,
social and cultural conditions and constraints,
and other infrastructure and accessibility aspects
at places of origin and destination. Studies
have generally indicated that migration occurs
mainly for economic reasons (Todaro, 1969, 1976,
1985; and Young, 1994). Economic motives, such
as the search for employment, improvements to,
and upgrading of, jobs, resulting in increased
wages and salaries, improvements in education
for employment-related needs and relocation
to gain close proximity to jobs are important
determining factors for migration. Skeldon(1992)
indicates that "migration allows the circulation
of goods, money and ideas, as well as people
sub-urban sectors. It concentrates a population
that can create a dynamic economy and society".
Consumption expenditure is one of the indicators
of the living status of a person.
Therefore
the main objectives of this study are:
i) to study the living status, that is, the
socio-economic characteristics of the migrants,
and
ii) to investigate the effects of some socio-economic
variables on migrants' living status through
linear regression analysis.
Data source
The data of this study was collected under the
project of “Strengthening the Department of
Population Science and Human Resource Development”
sponsored by United Nations Population Fund
(UNFPA). The pattern of data was collected in
three broad sections namely, fertility, mortality
and migration, along with socio-economic characteristics
of the respondents. The data of 505 respondents
was collected from the Katakhali Pourusova residential
area of Rajshahi District using the interview
method and by using purposive sampling technique,
with a set questionnaire. The 2004 voter list
of Katakhali Pourusova was used to identify
respondents.
Regression model
Methodology
Multiple
regression model expresses a dependent variable
as a function of several independent variables,
both qualitative and quantitative. Therefore,
a multiple linear regression model is considered
in this study and the form of the model is 
Where Xi is the regressor, a is constant, bi
is the parameters, Y is the dependent variable
and U is the stochastic error term of the model
such that U~NID(0,s2).
F-test
To
verify the overall significance of the regression
model as well as the significance of R2, an
F-test is used. The formula for F-test is
F= with (k-1, n-k) degrees of freedom
Where, n is the number of cases, K is the number
of parameters to be estimated and R2 is the
coefficient of determination (Gujarati, 2003).
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MODEL VALIDATION TECHNIQUE |
To check the stability
of the model, the cross validity prediction
power (CVPP), 
, is applied. Here
;
where, n is the sample
size or number of cases, k is the number of
predictors in the model and the cross validated
R is the correlation between observed and predicted
values of the dependent variable. The shrinkage
of the model is 
Shrinkage = 
- R2 
; where 
is CVPP & R2 is the coefficient
of determination of the model. Moreover, the
stability of R2 of the model is equal
to 1- shrinkage (Stevens, 1996).
Migration
& Age
Table 1 shows that the prime
ages for migrants was in the age range of
30-34 years. 22.80% heads of household of
Katakhali Pourusova migrated at the age of
30-34. In any age range above or below this
age group, the percentage is lower. This table
proves that the children and old age people
are less interested in migration. Most migrants
first leave their village at the lower end
of their working age period. This is probably
because the longer a migrant is expected to
remain in working life, the greater are the
number of years over which he/she can earn
extra returns from work after migration.
Occupation
From Table 2 it is seen
that among the total household heads most
of the migrants are service holders and businessmen
whose percentages were 44.8 and 26.5. This
is due to the fact that people of this area
are more literate and they are involved in
service and business rather than another occupation.
Economical requirements and human life is
interrelated. For this reason people employ
themselves in various jobs to have better
standards of living. Five distinct categories
of occupation were surveyed in this study
area. These categories were farmer, service,
business, labor and others (miscellaneous)
and are presented in Table 2.
Educational
Qualifications
From Table 3 it is found
that the illiterate are 15.85%. The signatory
rate is 3.7% whereas19.80% of household heads
had primary level education. 25.74% of household
heads had education up to class ten, 12.28%
had completed their school education. 9.91%
had completed their college studies and 6.91%
were graduates and 5.9% obtained M.Sc. degree
in this area. So we may conclude that migrants
live in moderately educated areas. In Bangladesh
there is a lack of opportunities for youths
to acquire sufficient, as well as better,
qualitative education in the rural sector.
They are devoid of such facilities, which
are necessary to raise their personality to
a level at par within this study area.
Migration
income pattern
From table 4 it is found that 17.4% of heads
of household had an income in the range of
Taka <2000 per month in this study area.
21% and 16.6% migrants earned taka 2500-3000
and 6000+ respectively. Others were shown
in Table 4. Agriculture
is the main sector for employment. They earn
most of the income from this sector. But this
sector cannot provide full employment to all
labor forces due to various reasons.
Stream
of migration
The migrants of heads of household were living
predominantly in urban areas. From Table 5
it is seen that the rural-rural area had 7.5%
of the migrants of heads of household, while
the 53.7% of the rural-urban migrants were
distributed. The urban-rural and urban-urban
were 3.8% and 35% migrants of heads of household.
The majority of both rural and sub-urban migrants
were living in urban to urban areas. Table
5 shows the extent and pattern of recent
rural-urban and intra-urban migration, and
urban-rural and intra-rural migration. Recent
rural-rural migration is more pronounced than
rural-urban migration. Slightly more destinations
than origin moved from urban-rural locations.
Recent rural-urban and urban-rural migration
was also significant. So we may conclude that
recent rural-urban migration, however, is
more pronounced than urban-rural migration.
However, it does not show any specific pattern
of step-wise migration; migrants do not necessarily
move from rural areas to small towns and from
there to a large city. The data shows that
sizeable proportions of migrants move from
rural areas to the largest urban centre.
Place
of migration
When the distance of place of migration from
the place of origin of the respondents is
concerned, the long-distance migration was
found in Table 6 among 53.5%
migrant population in high agricultural growth
areas, and the remaining 46.5% migrant population
in low agriculture growth areas. Short distance
migration is 34.7%. The pattern of long-distance
migration is generally rural-urban. The choice
of urban place by the migrants is generally
dependent on the ability of bearing the migration
cost, extent of risk that migrants take to
be successful and opportunities available
to the place, like easy contact with house,
availability of jobs and various amenities,
improved transport and communication facilities,
etc.
Cause
of migration
From Table 7 it is seen
that the maximum number of people migrated
for economic reasons, whose percentages were
88.7. The remaining few are due to the causes
of marriage, religious, educational and other
migrants, whose percentage were 4.4, 0.2,
0.2 and 6.5 respectively. So it is concluded
that maximum number of migrants had to migrate
to improve their financial condition. Migration
from the villages to the towns and cities
bears a close functional relationship with
the progress of industrialization, technological
advancement and other cultural changes which
characterize the evolution of modern society
in almost all parts of the world. It is due
not only to push of the villages and pull
of the towns and cities but also to the interaction
of several factors. When increasing population
in rural areas starts spreading into cities,
the influx of excess population occurs at
a much larger scale than the town and city
can absorb. Broadly speaking, migration of
people is a very common phenomenon. It can
result from many causes such as socio-economic,
political, cultural, natural calamities, and
so on, while the causes of migration from
rural to urban areas appear to be many. These
are the remarkable ones found in the Table
7.
Consumption
facilities of heads of household migrants
Many middle and upper middle class families
migrate to cities and towns for improving
their educational credentials and also to
get suitable employment, apparently in a quest
for social advancement and also to enhance
their status in the marriage market. For this
reason heads of households should lift up
their income which provided access to better
consumption. In general urban life provides
better facilities of various aspects so that
the migrant gets better opportunities through
consumption. From Table 8
it is seen that the multiple regression line,
educational qualification is positively related
to monthly consumption expenditure; the regression
coefficient is 92.877 with level of significance
0.000. Monthly income is positively related
to monthly consumption expenditure; the regression
coefficient is 0.497 with level of significance
0.000. Age at marriage has to positively relate
to monthly consumption expenditure; the regression
coefficient is 16.161 with the level of significance
0.543. Land before migration has to negatively
relate to monthly consumption expenditure;
the regression coefficient is -126.398 with
the level of significance 0.009. Land after
migration has to positively relate to the
monthly expenditure, the regression coefficient
is 72.636 with level of significance 0.049.
Therefore, the fitted regression model is
Y= 596.192+92.877X1+0.497X2+16.161X3-126.398X4+72.636X5
From
the above findings, the coefficient of determination
(R2) of this fitted model is 0.568, i.e. the
independent variables such as educational
qualification, monthly income, age at marriage,
land before migration, and land after migration
can explain 57% of the dependent variable,
that is, monthly consumption expenditure.
The calculated value of F-test is 131.22 with
(5, 499) degrees of freedom (d.f) but its
corresponding value is only 3.02 at 1% level
of significance. Moreover, the stability of
the fitted model is 56% and its shrinkage
is only 0.009582 where n is 505 and k is 5.
And the stability of R2 of this model is more
than 99%. Hence the model fits well. From
the above results it reveals that after migration
the heads of house holds have to achieve almost
a positive effect. It is found that educational
qualification, monthly income, age at marriage,
land before migration and land after migration
rises as monthly expenditure increases. Hence
it may be concluded that for increasing effects
of education, monthly income, age at marriage,
land before migration and land after migration
lift with the consumption level and monthly
expenditure. Before migration although they
had some land they do not have sufficient
consumption. So in this study migration plays
a positive role in developing the life status
of the migrants.
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CONCLUSION AND POILICY IMPLICATIONS |
In
the micro sense, migration behavior is an
individual's response to improve his/her economic
standing but in the macro sense migration
is interpreted as an adjustment of population
to economic and social change (ESCAP, 1982).
People migrate to a certain place with hopes
of improving their social, economic and health
status. These migrants have different levels
of aspiration as far as demographic condition
is concerned and the changes no matter how
insignificant have distinct factors attributed
to each, (at instances assisted by catalytic
agents). The major findings on the socio-economic
conditions of Katakhali Pourusova, based on
the questionnaire survey show that:
- The maximum
number of migrants have to migrate in the
age range 30-34 years.
- All most all
of the migrants have to migrate due to economic
reasons.
- The maximum
number of migrants have to migrate to improve
their life status. So they are obliged to
migrate to earn money.
Policy
implications
It is s difficult to formulate any easy and
simple solutions to solve the problem of destitute
people.
The following recommendations are suggested:
- The government
may invest resources for the improvement
of sub-urban economies through different
sub-urban development projects and by creating
job opportunities in the rural and sub-urban
areas.
- The government
should put emphasis on sub-urban industrialization.
This industrialization would be an instrument
of employment and income generation for
the sub-urban landless poor; present or
pre-employment migration already burdened
urban centers.
| Table
1: Age
of Migrant Heads of Household |
|
Age group
|
No. of migrants
|
Percentage (%)
|
|
15 - 19
|
14
|
2.80
|
|
20 - 24
|
74
|
14.7
|
|
25 - 29
|
90
|
17.8
|
|
30 - 34
|
115
|
22.8
|
|
35 - 39
|
111
|
22.0
|
|
40 - 44
|
68
|
13.5
|
|
45 – 49
|
33
|
6.50
|
|
Total
|
505
|
100.0
|
Back
to text
| Table
2:
Occupation of Heads of Household |
|
Occupation
|
No. of migrants
|
Percentage (%)
|
|
Farmer
|
22
|
4.40
|
|
Service
|
226
|
44.8
|
|
Business
|
134
|
26.5
|
|
Labor
|
112
|
22.2
|
|
Others
|
11
|
2.20
|
|
Total
|
505
|
100.0
|
Back
to text
| Table
3: Educational
Attributes of Heads of Household |
|
Educational level
|
No. of migrants
|
Percentage (%)
|
|
Illiterate
|
80
|
15.85
|
|
Signatory
|
18
|
3.70
|
|
Up to class v
|
100
|
19.80
|
|
Up to class x
|
130
|
25.74
|
|
SSC
|
62
|
12.28
|
|
HSC
|
50
|
9.91
|
|
B. Sc. degree
|
35
|
6.91
|
|
M. Sc. degree
|
30
|
5.90
|
|
Total
|
505
|
100.00
|
Back
to text
| Table
4: Monthly
Income Distribution for Heads of Household |
|
Range in Taka
|
No. of migrants
|
Percentage (%)
|
|
<2000
|
88
|
17.4
|
|
2000-2500
|
40
|
7.9
|
|
2500-3000
|
106
|
21.0
|
|
3000-3500
|
38
|
7.5
|
|
3500-4000
|
61
|
12.1
|
|
4000-4500
|
7
|
1.4
|
|
4500-5000
|
50
|
9.9
|
|
5000-5500
|
0
|
0.00
|
|
5500-6000
|
31
|
6.1
|
|
6000+
|
84
|
16.6
|
|
Total
|
505
|
100.00
|
Back
to text
|
Table
5: Recent
Pattern of Stream of Migration for Heads
of Household
|
|
Types of migrants
|
No. of Migrants
|
Percentage (%)
|
|
Rural-Urban
|
271
|
53.7
|
|
Rural-Rural
|
38
|
7.5
|
|
Urban-Rural
|
19
|
3.8
|
|
Urban-Urban
|
177
|
35.0
|
|
Total
|
505
|
100.0
|
Back
to text
|
Table
6:
Distribution of Migrants According to
the Place Of Origin From the Place of
Destination for Heads of Household
|
|
Place of migration (per kilo)
|
No. of migrants
|
Percentage (%)
|
|
0-20
|
175
|
34.7
|
|
21-40
|
33
|
6.5
|
|
41-60
|
27
|
5.3
|
|
61+
|
270
|
53.5
|
|
Total
|
505
|
100.00
|
Back
to text
|
Table
7: Cause
of Migration for Household Heads
|
|
Cause of migration
|
No. of Migrants
|
Percentage (%)
|
|
Economic
|
448
|
88.7
|
|
Religious
|
1
|
0.2
|
|
Education
|
1
|
0.2
|
|
Marriage
|
22
|
4.4
|
|
Others
|
33
|
6.5
|
|
Total
|
505
|
100.00
|
Back
to text
|
Table
8: Results
of Regression Model for Heads of Household
|
|
Variables
|
Unstand. Coefficient
|
Significance
|
|
|
Stand. Error
|
|
Constant
|
596.192
|
412.482
|
0.149
|
|
Educational (X1)
|
92.877
|
16.723
|
0.000
|
|
Monthly income (X2)
|
0.497
|
0.024
|
0.000
|
|
Age at marriage (X3)
|
16.161
|
26.521
|
0.543
|
|
Land before migration (X4)
|
-126.398
|
48.050
|
0.009
|
|
Land after migration (X5)
|
72.636
|
36.787
|
0.049
|
|
Sample size
|
505
|
|
R2
|
0.568
|
Dependent variable
(Y): monthly consumption expenditure
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