Socio Economic Study of Rickshawpullers in Dhaka City

8/14/2019 Socio Economic Study of Rickshawpullers in Dhaka City

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1.1 Origin of the Report:

This report is a primary requirement for the course F-503: Business Statistics under ourhonorable instructor Prof. r. !. "ahan#ir $lam %ho&dury at the '!B$ Pro#ram( epartment

of Finance) ha*a +ni,ersity. $ sur,ey &as carried out to analye the socio-economic status ofthe ric*sha& pullers of ha*a city.

This report is also aimed to inte#rate our classroom learnin# &ith the real life practices.

1.2 Objectives of the Report:

$s a part of course F-503: Business Statistics( &e had the opportunity to study on the socio-economic status of ric*sha& pullers in ha*a city.

The primary obecti,es of the report are to:/. $sses the socio-economic status of ric*sha& pullers in ha*a city.

The secondary obecti,es are:/. +se of different statistical tools. +nderstandin# the different statistical tools used in the study

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1.3 Sources of Data:

For the purpose of this report data ha,e been collected both from primary and secondary sources.Primary Source:

For easier understandin# our #roup members tried to co,er different areas ofha*a city for the study. The sample sie of the present study comprised of 5ric*sha& puller. 1e choose them by simple random samplin#. The rele,antinformation collected from primary sources is collected by sur,ey questionnairepro,ided by our course teacher.

Secondary Source:

The secondary source of data collection is internet.

1. !ethodo"ogy:

For a socioeconomic study to be or#anied and conducted properly( it should be systematic( andha,e minimum bias( and allo& for consistent comparison and reasoned ud#ment. For all this tobe accomplished( carefully planned( ,ery precise and lo#ical &or* methodolo#ies are usuallyde,eloped. $ccordin#ly( a methodolo#y &as de,eloped for the study on socioeconomic status of2ic*sha& pullers in ha*a city. This methodolo#y consists of se,eral statistical analyses.

#he first sectionaims to pro,ide a comprehensi,e profile for the study site and de,elop

criteria for socioeconomic assessment. This could be attained throu#h de,elop a thorou#hbac*#round about 2ic*sha& pullers and their socioeconomic assessment.

#he second sectiondeals &ith conductin# the socioeconomic assessment( &hich be#ins&ith the identification of main issues in the study and follo&ed by identification by of mainsocioeconomic aspects. %riteria for socioeconomic assessment should be firstly de,eloped. Thede,elopment of such criteria should be based on the list of socioeconomic impacts andindicators.

Socio-economic status of ric*sha& pullers &as calculated on the basis of daily income

composition( educational le,el( family type and sie( land holdin#( housin# pattern and materialpossession.

#he fina" and third sectionof socioeconomic study is intended to pro,ide some basicfindin#s of the ric*sha& pullers socio-economic status.

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t should be borne in mind that the implementation of the socioeconomic study in,ol,es anumber of field &or* acti,ities( &hich include field sur,eys to acquire data and information fromprimary sources. This data and information is mainly concerned &ith personal opinions( &hichcannot be obtained from secondary sources such as perceptions and opinions of the indi,idualsabout their concerns and future perspecti,es of the study site. They &ill also be utilied in the

,erification of data and information obtained from secondary sources.

The form of sur,ey to be conducted &ill depend upon direct inter,ie&s &ith the ric*sha&pullers in order to ensure hi#h rate of response. t also helps to ensure the seriousness of theans&ers #i,en by the inter,ie&ees. !oreo,er( the sensiti,ity of some questions and the need forclear( sharp and e4plicit ans&ers to some questions compel the need for personal inter,ie&approach.

$lso( as a result of impossibility of in,esti#atin# all population( samplin# method &ill be

useful to acquire data and information to sa,e time and effort. To ensure the representati,e of thesample( the samplin# should be random. This &ill ensure that different socioeconomic #roups areincluded in the sample.

The best representati,eness of the sample for the population can be considered as afunction of sample sie and samplin# method. This means that the pre-determination of thesample sie is meanin#less. So the decision of the sample sie should be ta*en durin# thecarryin# out of the sur,ey( ta*in# into account time( effort and fund limitations.

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Figure 1: The Study Process

1.$ %imitations of the Report:

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n preparin# this term paper( &e faced some difficulties in #atherin# appropriate information andother related issues. t &ould ha,e been helpful for us to analye the entire scenario if &e hadconducted more sur,eys. espite these limitations &e tried our best to collect necessary data andinformation that ha,e been analyed in the subsequent section.

The focusin# limitations of this study &ere as follo&s:

The main limitation of the study is that( the 2ic*sha& pullers did not disclose their

economical information li*e) salary( total family income( family members informationand their land area in ,illa#e and o&nership information of different household items.

The number of sur,ey sample sie is small( so our findin#s may not depict the &hole

picture.

The report &as prepared &ithin a ,ery short time considerin# the topics related to it.

Thats &hy) it &as not possible to demonstrate all aspects of the report.

2.1 Socio&economic study of Ric'sha( pu""ers of Dha'a )ity:

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62%7S8$1 is the traditional Ban#ladeshi ride. 2ic*sha& can be found in almost e,ery&herein Ban#ladesh. The people &ho earn their li,elihood by pullin# ric*sha& are called the ric*sha&puller. +sually( they are poor people. The ma4imum numbers of ric*sha& plays on the roads andstreets of ha*a city. n ha*a ric*sha& is ,ery common ride. So ric*sha& pullers are also

common in ha*a city. $ 2ic*sha&-puller starts pullin# his 2ic*sha& early in the mornin# andhe *eeps on pullin# throu#hout the day to earn his li,in# but still( &hat he earns is not enou#h tosatisfy the daily needs of his entire family. Therefore the rest of his families often ha,e to &or*to support him to bear the e4penses of the &hole family.

This study has been done to assess the socio-economic status of ric*sha& pullers in ha*a cityusin# different statistical techniques.

2.2 Data *na"ysis and Discussion:

ata &as collected throu#h personal inter,ie& method about a#e composition( educational le,el(family type and sie( land holdin#( housin# pattern( daily income( material possession andanalyed for percenta#e and mean. Then socio-economic status &as classified as lo&( mediumand hi#h after quantifyin# the factors as &ell as calculatin# standard de,iation and mean.

2.2.1 *ge composition:

$#e refers to the chronolo#ical a#e of the ric*sha& pullers in years at the time of inter,ie&.

Table 1: Distribution of Rickshaw pullers according to Age composition

S". +o. *ge )"ass +o. of Ric'sha( pu""ers

/ 0-9 9-3 /53 3-;; 3; ;;-5 35 5-0

!inimum of the ric*sha& pullers &ere in the lo&est =0-9? and hi#hest =5-0? a#e ran#e. />of the ric*sha& pullers &ere fallin# in the a#e ran#e of 3-;; years and ;;-5 years.

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Figure : Age !omposition of Rickshaw pullers

2.2.2 )"assification according to home districts:

8ome districts refer to the ric*sha& pullers permanent address. n our sur,ey &e ha,e as*ed theric*sha& pullers about their home district to find out the ric*sha& pullers home districtcomposition in ha*a city.

Table : !lassification according to home districts

,ome District +o of Ric'sha( pu""ers

2an#pur Shat*hira /@ola /Aarayan#an /*uri#ram /!ymensin#h 37ishore#an "amalpur /7ushtia 3

"assore /Aatore /#ota" 2$

!ost of the ric*sha& pullers are mi#rants from other districts. From our field sur,ey( &e ha,efound that of the ric*sha& pullers came from 2an#pur and 7ishore#an area respecti,ely &hich

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is ;> of the total number. 3 of the ric*sha& pullers came from !ymensin#h and 7ushtiarespecti,ely &hich is />. $nd only ;> of the ric*sha& pullers came from Shat*hira( @ola(Aarayan#an( 7uri#ram( "amalpur( "assore and Aatore area.

Figure ": #ome District composition of the Rickshaw pullers

2.2.3 )omposition of years in current profession:

Table ": $ears in current profession

-ears +o. of Ric'sha( pu""ersBelo& 5 years 35 years and abo,e ///0 years and abo,e /00 years /

$lmost ;;> of the ric*sha& pullers are in their profession for 5 years and abo,e. ;0> of theric*sha& pullers are in their profession for /0 years and abo,e. 1e ha,e found only onerespondent in his profession for 0 years.

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Figure %: !urrent years in profession

2.2. Status in profession:

Table %: Status in profession

Status +o of Samp"es

nly puller 5Both puller and o&ner 0

$s per the table( all of the ric*sha& pullers do not ha,e their o&n ric*sha&s. 1e did not find anyric*sha& o&ner( &ho o&ns and also pulls ric*sha&. $s our sample sie is not lar#e enou#h( thismay not be the scenario if &e could conduct our sur,ey in lar#e sample sie.

2.2.$ ncome composition:

1e ha,e collected the ric*sha& pullers daily income =&ith o&ners rent? and daily income=&ithout o&ners rent? for our analysis( &hich is cate#orically arran#ed in the follo&in# table.

Table &: 'ncome composition (with owners rent)

ncome +o. of Ric'sha( pu""ers Percentage

T*. 00 and belo& ;.00>$bo,e T*. 00 C 9.00>T*. 300 and abo,e /0 ;0.00>T*. ;00 9.00>

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$s per the table( ;0> of the ric*sha& pullers in our sample earn t*. 300 and abo,e per day( ;>earn t*. 00 and belo&( 9> earn abo,e t*. 00. nly 9> of the ric*sha& pullers earn t*. ;00per day.

n the belo& table &e cate#oried daily income of the ric*sha& pullers &ithout ric*sha& o&ners

rent or ta*e home income.

Table *: 'ncome composition (without owners rent)

ncome +o. of Ric'sha( pu""ers Percentage

T*./00 and belo& C 9.00>$bo,e T*. /00 ;.00>T*. 00 and abo,e // ;;.00>T*. 300 / ;.00>

From our sur,ey( ;;> of the ric*sha& pullers earn t*. 00 and abo,e( 9> of the ric*sha&

pullers earn t*. /00 and belo&( ;> of the ric*sha& pullers earn abo,e t*. /00. nly ;> of theric*sha& pullers earn t*. 300.

Figure &: 'ncome composition

2.2./ %iterate member in a fami"y

n this section &e &ill identify ho& many of the ric*sha& puller ha,e a literate family members.

)ategory +o. of Ric'sha( pu""ers

literate 0lliterate 5#ota" 2$

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From our study( &e found that( 90> of the ric*sha& puller has a literate family member( &hichis ,ery si#nificant.

2.2.0 Schoo" going members in the fami"y

$s &e collected family members information of the ric*sha& pullers( &e &ill find out ho&many members of the ric*sha& pullers are #oin# to school and ho& many are not. From thisanalysis( &e can identify their family educational bac*#round.

Table +: School going members in the family

)ategory +o. of Ric'sha( pu""ers

School #oin# members 9Aon-school #oin# members /C#ota" 2$

1e ha,e found that( only 9 ric*sha& pullers ha,e school #oin# family members( li*e: son(dau#hter( brother or sisters. But &hich is si#nificant from the study is that( /C of the ric*sha&pullers ha,e no family members &ho are #oin# to school.

2.2. !ateria" Possession:

perationally defined as the #eneral materials possessed by the ric*sha& pullers) includin#recreational( machinery( domestic material( and communication and transport materials.

Table ,: -aterial Possession!ateria"s +o of Ric'sha( pu""ers

!obile 52adio ;T&in / T@ %ycle !otor %ycle 0Bed 5Sho& %ase 9

ressin# Table 9Se&in# !achine ;

Table 9 re,eals that( maority of the ric*sha& pullers ha,e bed in their home. But the interestin#findin# is that( e,ery respondents of our sur,ey o&ns a mobile( &hich they use forcommunication and also for their entertainment purpose.

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Figure *: -aterial Possession

3.1 Statistica" *na"ysis:

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n this part of the socio economic study &e &ill try to use different statistical techniques toanalye our ra& sur,ey data.

3.2.1 Describing Data:

3.2.2. +umerica" !easures of *ge:

Table .: Age

Age

!ean 3./Standard 'rror /.CC0C5;9!edian 3;!ode 35Standard e,iation 9.9539/C39

Sample @ariance C9.3D7urtosis /.39/;0/5S*e&ness /./35;9032an#e 39!inimum !a4imum 0Sum D0;%ount 5

+sin# statistical analysis tool &e found that( the mean a#e of the ric*sha& pullers is 3./ years.

1e *no& that( mean is unduly affected by unusually lar#e and small ,alues. From our data sheetit is clear that( there are no unusual ,alues. So( &e can say that( our mean 3./ years actuallysho&in# the characteristics of out samples.

Figure +: (Age) -ean/ -edian/ -ode

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From table D &e found that( the median a#e of the ric*sha& pullers is 3; years. The mode is 35years.

!easure of dispersion:

$ measure of location( such as mean or the median( only describes the center of the data. t is,aluable from the standpoint( but it does not tell us anythin# about the spread of the data.

Range:From table D &e found that( the ran#e is 39 years. t is the difference bet&een the lar#estand the smallest ,alues in the data set.

Standard Deviation:The standard de,iation of the a#e data set is 9.95( &hich means that ourdata are close to the arithmetic mean.

3.2.3. +umerica" !easures of ncome (ith o(ners rent:

Table 10: 'ncome

'ncome

!ean 9Standard 'rror /.055;C55!edian 50!ode 300Standard e,iation 0.CC/3CC3

Sample @ariance 333.3333337urtosis -0./;30C;;5;S*e&ness 0.5D3;DC9/2an#e 00!inimum 00!a4imum ;00Sum C00%ount 5

The a,era#e income =&ith o&ners rent? of the ric*sha& pullers is t*. 9. The median income is

t*. 50( and the mode income is 300.



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Table ,: ('ncome) -ean/ -edian/ -ode

Range:From table/0 &e found that( the ran#e is t*. 00. t is the difference bet&een the lar#est

and the smallest ,alues in the data set.

Standard Deviation:The standard de,iation of the a#e data set is 0.9( &hich is quite lar#e.

3.2.. +umerica" !easures of ncome (ithout o(ners rent:

Table 11: 'ncome

'ncome (without owners rent)

!ean /5.Standard 'rror /.3DC3//5;!edian /50!ode 00Standard e,iation /.D955C9Sample @ariance 39;.3333337urtosis -0.;900/D3D3S*e&ness 0.5DDD;/2an#e 0!inimum 90!a4imum 300

Sum ;/;0%ount 5

The a,era#e income =&ithout o&ners rent? of the ric*sha& pullers is t*. /5.0. The medianincome is t*. /50( and the mode income is 00.

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Figure .: 'ncome (-ean -edian/ -ode)

Range:From table // &e found that( the ran#e is t*. 0. t is the difference bet&een the lar#estand the smallest ,alues in the data set.

Standard Deviation:The standard de,iation of the a#e data set is /.D9( &hich is quite lar#e.

3.2.$. +umerica" !easures of %and area in vi""age:

Table 1: 2and Area

2and Area

!ean ;.99Standard 'rror 0.D99903DD/!edian 5!ode 0Standard e,iation ;.D;;0/DD5CSample @ariance ;.;;333333

7urtosis -.0/0CCD9S*e&ness 0.0909C;992an#e /!inimum 0!a4imum /Sum /%ount 5

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The a,era#e land area of the ric*sha& pullers in ,illa#e is ;.99 decimal. The median land area is5.00 decimal( and the mode land area is 0.00 decimal.

Figure 10:2and area (-ean -edian/ -ode)


Range:From table / &e found that( the ran#e is / decimal. t is the difference bet&een thelar#est and the smallest ,alues in the data set.

Standard Deviation:The standard de,iation of the a#e data set is ;.D; decimal( &hich indicatesthat the standard de,iation is closer to arithmetic mean of the data set.

3.2./. +umerica" !easures of Room number in vi""age:

Table 1": Room 3umbers

Room numbers

!ean .5Standard 'rror 0.5;5;/!edian 3!ode ;Standard e,iation /.C309Sample @ariance /.5D3333333

7urtosis -0.5/33/39S*e&ness -0.;5;/D0C/C2an#e ;!inimum 0!a4imum ;Sum 3%ount 5

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The a,era#e room numbers of the ric*sha& pullers in ,illa#e is .5. The median room numberis 3.00( and the mode land area is ;.


Range:From table /3 &e found that( the ran#e is ;. t is the difference bet&een the lar#est andthe smallest ,alues in the data set.

Standard Deviation:The standard de,iation of the a#e data set is /.( &hich indicates that thestandard de,iation is closer to arithmetic mean of the data set.

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.1 Re"ationship bet(een years in profession and per day actua" income:

There may be a common perception that( if a ric*sha& puller &ho is pullin# ric*sha& for manyyears( may ha,e a hi#her income than a ric*sha& puller &ho is pullin# ric*sha& for a fe& years.

n this section &e &ill try to find out if there is a relation bet&een the ric*sha& pullers years inprofession and their actual income per day =&ithout o&ners rent?. For this analysis &e &ill use%orrelation and Einear 2e#ression analysis.

First( &e ha,e to define the dependent and independent ,ariables. 8ere(

ndependent variab"e:ears in professionDependent variab"e:ncome =per day?

Table 1"

SUMMARYOUTPUT

Regression Statistics

Multiple R 0.43

R Square 0.18

Adjusted RSquare 0.15

Standard Error 57.26

!ser"ations 25

ANOVA

df SS MS FSignifica

nce F

Re#ression 1 16815.3116815.

31 5.13 0.03

Residual 23 75400.693278.2

9

$otal 24 92216

Coefficients

StandardError t Stat P-value

Lower95%

Upper95%

%nter&ept 215.39 24.79 8.690.000000

010 164.11 266.67

' (aria!le 1 )5.78 2.55 )2.260.033258

169 )11.07 )0.50

From the abo,e summary output( &e found 2 Square &hich is the coefficient of determination(&hich e4plains the proportion of the total ,ariation in the dependent ,ariable that is e4plained(or accounted for( by the ,ariation in the independent ,ariable.

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%oefficient of determination =2 square? < 0./9

'nterpretation: 1e can say there is a positi,e relationship bet&een the t&o ,ariables. /9> of,ariation in the income per day is e4plained or accounted for( by the ,ariation in the years in

profession of a ric*sha& puller.Regression 45uation:

G < a H bI

8ere( a < -interceptb < Slope of the re#ression line

From the table /3( &e can find our re#ression line( &hich is

G < /5.3D J 5.C9;I

Figure 11: 2inear Regression 2ine 1

The equation indicates that( a / year of profession increase of the ric*sha& puller &ill result in a

t* 5.C9 decrease in income per day. t may not be true for the &hole population. The a J ,alueindicates that( if there is no years of profession then a ric*sha& puller per day income is t*./5.3D( &hich can not be true.

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.2 Re"ationship bet(een Ric'sha( pu""ers age and per day actua" income:

n this section &e &ill try to find out if there is any relationship bet&een the ric*sha& pullers a#eand their per day income. For this analysis &e &ill use %orrelation and Einear 2e#ressionanalysis.


ndependent variab"e:$#eDependent variab"e:ncome =per day?

Table 1%

SUMMARY

OUTPUT


Multiple R 0.33

R Square 0.11


Standard Error 59.85

!ser"ations 25

ANOVA

df SS MS FSignifica

nce F

Re#ression 1 9838.979838.

97 2.75 0.11

Residual 23 82377.03

3581.

61$otal 24 92216.00

Coefficients

StandardError t Stat

P-value

Lower95%

Upper95%

%nter&ept 248.29 51.31 4.840.0000

70 142.15 354.43

' (aria!le 1 )2.29 1.38 )1.66 0.1110 )5.14 0.57

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11


%oefficient of determination =2 square? < 0.//

'nterpretation: 1e can say there is a positi,e relationship bet&een the t&o ,ariables. //> of,ariation in the income per day is e4plained or accounted for( by the ,ariation in the a#e of aric*sha& puller.Regression 45uation:

G < a H bI



G < ;9.D J .DI

Figure 1: 2inear Regression 2ine

The equation indicates that( a / year of a#e increase of the ric*sha& pullers a#e &ill result in a t*.D decrease in income per day. t may not be true for the &hole population. The a J ,alueindicates that( if there is no years of a#e then a ric*sha& puller per day income is t*. ;9.D(&hich also can not be true.

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.3 Re"ationship bet(een Ric'sha( pu""ers income and "and possession in vi""age:

n this section( &e &ill try to find out if there is any relationship bet&een a ric*sha& pullers per

day income and their land possession in their ,illa#e. n our sur,ey( &e found that( /3 of theric*sha& pullers ha,e their o&n land in ,illa#e area and the rest / ha,e no land in theirpossession.


ndependent variab"e:ncome =per day?Dependent variab"e:Eand area in possession

Table 1&

SUMMARY

OUTPUT


Multiple R 0.70

R Square 0.49


Standard Error 3.61

!ser"ations 25

ANOVA

df SS MS FSignifica

nce F

Re#ression 1 286.85 286.85 22.01 0.00

Residual 23 299.79 13.03

$otal 24 586.64

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Coeffici

entsStandard

Error t Stat P-

valueLower95%

Upper95%

%nter&ept 14.12 2.10 6.73 0.00 9.78 18.45

' (aria!le 1 )0.06 0.01 )4.69 0.00 )0.08 )0.03


%oefficient of determination =2 square? < 0.;D

'nterpretation: 1e can say there is a positi,e relationship bet&een the t&o ,ariables. ;D> of,ariation in the land possession is e4plained or accounted for( by the ,ariation in the income perday of a ric*sha& puller.

Regression 45uation:

G < a H bI



G < /;./ J 0.0I

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Figure 1: 2inear Regression 2ine "

The equation indicates that( a t*. / increase of the ric*sha& pullers income per day &ill result ina t* 0.05 decrease in land possession. t may not be true for the &hole population. The a J ,alueindicates that( if there is income then a ric*sha& puller land possession is /;.// decimal( &hich

also may not be true.

$.6 Reference

1. Eind( $ ou#las( !archal( K. 1illiam( 1athen( $. Samuel =0//?. StatisticalTechniques in Business & Economics. Ae& elhi: Tata !cKra&-8ill Publishin#%ompany Eimited.

2. Standard de,iation =0/3?. 2etri,ed $pril /0( 0/3From http:LLen.&i*ipedia.or#L&i*iLStandardMde,iation

3. %orrelation coefficient. =0/3?. 2etri,ed $pril /0( 0/3From http:LLen.&i*ipedia.or#L&i*iL%orrelationMcoefficient

. Statistics.=0/3?. 2etri,ed $pril /0( 0/3From https:LLen.&i*ipedia.or#L&i*iLStatistics
http://en.wikipedia.org/wiki/Standard_deviationhttp://en.wikipedia.org/wiki/Correlation_coefficienthttp://en.wikipedia.org/wiki/Correlation_coefficienthttps://en.wikipedia.org/wiki/Statisticshttps://en.wikipedia.org/wiki/Statisticshttp://en.wikipedia.org/wiki/Standard_deviationhttp://en.wikipedia.org/wiki/Correlation_coefficienthttps://en.wikipedia.org/wiki/Statistics

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Socio Economic Study of Rickshawpullers in Dhaka City