Cultural Theory of Poverty and Informal Sector: A Case Study of Street Vendors of Pondy Bazaar, Chennai

 

Shweta Sharma

Assistant Professor, Department of Planning, School of Planning and Architecture,

Vijayawada, Andhra Pradesh, India

*Corresponding Author Email: shweta@spav.ac.in

 

ABSTRACT:

Poverty has ideally been studied from either the individual perspective or the cultural perspective. While the individual perspective holds genetic factors to be responsible for poverty of urban dwellers, the cultural theory holds the societal, political, cyclical and geographical factors to be responsible for poverty of people. This research paper tests the cultural theory of poverty on the street vendors of PondyBazaar area in a Tier I city, Chennai. The impact of societal factors (gender, religion, dependents, choice of occupation and choice of migration), economic factors (occupation before migration), cyclical factors (willingness to stay in the same occupation) and geographical factors (distance of vendors’ native place to Chennai) was studied on income of street vendors through a structural equation model. Questionnaire method was used to collect data from 100 street vendors spread over Sivagnanam Street, Sivaprakasam Street and Sir Thyagaraya Road in PondyBazaar area. The results depict that the structural equation was highest for income of vendors. Also, three of the chosen societal factors (gender, choice of occupation and choice of migration), economic factors and cyclical factors have a major impact on determining the incomes of vendors. However, two societal factors and geographical factors have very less impact on earnings of vendors. Since the test results were run on Tier I city, the theory might or might not hold true in a Tier II city which needs to be tested in further research.

 

KEYWORDS: Street vendors, cultural theory, societal factors, geographical factors, economic factors, cyclical factors, Pondy Bazaar, Chennai.

 

 


1. INTRODUCTION:

Poverty has been defined primarily from the point of view of either the individual or the society over a period of time. Thus there have been two prominent theories explaining the rationale for the existence of poverty in the world: Individual theories and Cultural theories. Individual theories emphasise the role of the individual factors for being poor such as lack of genetic qualities to come out of poverty or their laziness which repels them to acquire education and thus get a good job in life.

 

There have been studies in the past which have tested this individual theory of poverty in the context of street vendors with regard to Tier I (Sharma 2018) and Ties II cities (Sharma 2017) of India. There is a second set of theories which are referred to as the cultural theory of poverty. The cultural theory of poverty can be subdivided into various sub theories of poverty, viz. the cultural belief systems supporting sub-cultures of poverty, economic, political and social distortions or discrimination leading to poverty and geographical disparities or cumulative and cyclical interdependencies being responsible for poverty of people.

 

Cultural belief systems assume that poverty is transmitted as a set of beliefs and values socially generated and passed from generation to generation. The proponents in this theory believe that the source of poverty is the economic, political, and social system which provides limited opportunities and resources to people to achieve income and well-being. Also, people, institutions, and cultures in certain areas lack the resources needed to generate well-being and income imply because of their geographical location. The cumulative theory of poverty looks at individuals and community caught in a spiral of opportunity and problems which closes their opportunities and creates set of problems making their responses to counter poverty impossible (Bradshaw 2000). Similarly, cyclical theory of poverty links individual situations to community resources, where a weak economy makes economic survival harder for individuals who lack resources to participate in the economy as they are able to pay fewer taxes. This theory of “interlocking, circular, interdependence within a process of cumulative causation” that helps explain economic underdevelopment and development (Myrdal 1957). Myrdal establishes that the negative events such as closure of a factory or economic crisis can lead to a spiral of problems of migration of residents from a village or small town. Thus this spiral continues and leads to acute poverty in the communities facing these problems.

 

This research paper explores the plausibility of cultural theory in explaining the reasons for poor earnings of street vendors in Tier I city of India; Chennai. Gender (male or female), number ofdependents, Religion (Christian, Muslim or Hindu) Choice of occupation (society pressure, family pressure, family business, own choice) as vending and choice of migration (own choice, pressure from family members, relatives and acquaintances) from their native place to the Chennai have been used as dummy variables to represent societal pressures which compel one to opt for vending as propagated by cultural theory. Willingness to stay (yes or no) in same occupation of vending has been chosen as a proxy variable for cyclical theory of poverty where a weak economy with lack of opportunities for vendors compels them to opt for the same occupation over an extended period of time. Distance of migration (0-200 kms, 200-400 kms, 400-600 kms, 600-800 kms And above 800 kms) refers to the geographical theory of migration where locations far off from the major city suffer more and thus have a bearing on migration of vendors and their earning patterns.

 

2. OBJECTIVE AND RESEARCH QUESTIONS:

The objective of this paper is to examine how the cultural factors affect the earnings of vendors. These cultural factors can be either societal pressures, geographical reasons or economic parameters to compel them to opt for street vending. The research questions framed to explore this theory are as follows:

a)      What is the impact of societal factors (gender, religion, dependents, choice of occupation and choice of migration) on income of street vendors?

b)      What is the impact of economic factors (occupation before migration) on income of street vendors?

c)      What is the impact of cyclical factors(willingness to stay in the same occupation) on income of street vendors?

d)      What is the impact of geographical factors (distance of vendors’ native place to Chennai) on income of street vendors?

e)      Which factors in the gamut of cultural theories has the greatest influence on income of street vendors?

 

3. CASE AREA:

In 2006, a committee under the chairmanship of Justice Mr. Kanakarajidentified hawking zones on the basis of a survey carried out in 10 zones of the city. The committee formed a scheme to formalise existing street vendor markets and regulate hawkers in these 10 zones of the city. However the scheme did not allow for entry for new street vendors. The chairman of the Implementation Committee (chaired by Justice Mr. Ramamurthy) filed a report 2008 reviewing the work of officials of Chennai Municipal Corporation and police in this regard ad highlighted the gloomy picture of unclaimed hawking spaces, encroaching hawkers in other areas of the city and officials failing in their duty. In 2009 the Ministry of Urban Development and Poverty Alleviation circulated the National Policy on Urban Street Vendors draft Bill. The Bill was finally approved in 2014 and it became the Street Vendors (Protection of Livelihood and Regulation of Street Vending) Act, 2014. The greater Chennai Corporation started the work of enumeration of hawkers across all the 15 zones mandated by the Tamil Nadu Street Vendors (Protection of Livelihood and Regulation of Street Vending) Scheme and Rules, 2015. 27,000 street vendors were surveyed and issued licenses. The Town Vending Committees were constituted as per Rule 11 of the Tamil Nadu Street Vendors (Protection of Livelihood and Regulation of Street Vending) Rules 2015 which advocated large scale relocation of hawkers from streets of Mylapore and T.Nagar to the shopping complexes (Shekhar 2012).

 

The study area for the research is Pondy Bazar in T. Nagar (Kodambakkam Zone X, Thyagaraya Nagar Ward 136). Chennai Corporation moved over 600 shops off the pavements to the Corporation Complex in Pondy Bazaar in November 2013 (Lopez 2017). However again the street vendors re-occupied the Sivaprakasam Street, Lakshmikanthan Street and Dr.Singaravelu Street as well as the arterial Sir Thyagaraya Road. Shopkeepers cite lack of clientele, inadequate infrastructure facilities in the complex and the high charges to be paid to occupy the complex premises for returning to the pavements. Today the complex has only 240 shops as most of the pavement vendors refused to move here as their livelihood was completely dependent on commuters using these roads. Also the shop-keepers who have moved to the complex are also suffering (Raqshan 2017).

 

The survey for the research was done for 100 street vendors spread over Sivagnanam Street (0.31 km), Sivaprakasam Street (0.33 km) and Sir Thyagaraya Road (1 km).

 

 

Fig. 1: Location of Zone X and Ward 136 in Chennai

 

 

Fig. 2: Pondy Bazaar area boundary and the streets surveyed

4. METHODOLOGY:

a) Participants

The sample consisted of 100 street vendors located in a prominent markets of Chennai city: One town and Besant Road. The sample was selected with the method of purposive sampling where data was collected only for registered vendors in the chosen markets.

 

b)Data collection and analysis:

The data was collected from primary and secondary sources. The data collected through secondary sources was the location map of vendors and their registration details. The primary data was collected through questionnaires. The closed and open ended questionnaire was chosen as substitute to interviews because semi-structured interviews would not help in getting in-depth understanding of the vendors. The closed ended questionnaire included questions about the socio-economic conditions of vendors. Likert scale was used to assess certain qualitative questions. In conclusion, the data was collected through both qualitative and quantitative methods to cross check the reliability and validity of results collected.

 

c) Design and procedure:

The data was collected through the questionnaire in two weeks. Post data collection from the field, path analysis was done to understand the impact of Gender (G), Religion (R) and Dependents (D) on Choice of Occupation (CO), Choice of Migration (CM), Distance of Migration (DM) and Occupation before Migration (OBM). Later impact of all the above mentioned parameters was analysed on Willingness to Stayin Vending (WSV). Finally impact of G, R, D, CO, CM, DM, OBM and WSV was analysed on Income (I) of vendors.

 

In the diagram (figure 1), G, R and D are considered to be exogenous variables, that is, their variance is assumed to be caused entirely by variables not in the causal model.

 

The connecting line with arrows at both ends indicates that the correlation between these three variables will remain unanalysed because we choose not to identify one variable as a cause of the other variable. Any correlation between these variables may actually be casual and/or may be due to these variables sharing common causes. For example, the co-variance between G, R, D is shown on the arrows.

 

G, R, D, CO, CM, DM, OBM and WSV and I are endogenous variables in this model-their variance is considered to be explained in part by other variables in the model. Paths drawn to endogenous variables are directional (arrowhead on one end only).

 

Variance in CO, CM, DM, OBM and WSV are theorized to result from variance in G, R, D and extraneous (not in the model) sources. The influence of these extraneous variables is indicated by the arrow from EY. Variance in income is theorized to be caused by variance in age, education, and extraneous sources.

 

For each path to an endogenous variable a path coefficient, pij, has been calculated, where "i" indicates the effect and "j" the cause. If we square a path coefficient we get the proportion of the affected variable's variable. The coefficient may be positive

 

(increasing the causal variable causes increases in the dependent variable if all other causal variables are held constant) or negative (increasing causal variable decreases dependent variable).

 

d)       Ethics

The ethical principles of scientific research have been followed. The data of research were not distorted. The data was analysed and interpreted, avoiding any possible bias and prejudices, respecting objectivity. As for the conduct of the research, the purpose of research was explained to protect the human subjects, respecting their autonomy and privacy.

 

 


 

 

Fig. 3: Structural Equation Model


 

5.      RESULTS:

Path analysis has been conducted as a hierarchical (sequential) multiple regression analysis. For each endogenousvariable a multiple regression analysis was done predicting income (Y) from all other variables (age, education andplace of stay) which are hypothesized to have direct effects on Y. We do not include in this multiple regression anyvariables which are hypothesized to affect Y only indirectly (through one or more intervening variables). The betaweights from these multiple regressions are the path coefficients shown in the typical figures that are used to displaythe results of a path analysis.

 

Path co-efficient model for Pondy Bazaar is illustrated in Figure 2, to which path coefficients have beencomputed as below:

 

Path co-efficient for gender on distance of migration:

Direct: 0.74

Unanalysed due to religion:

-0.08x-0.15=0.012

Unanalysed due to dependents:

-0.07x0.11=-0.008

Total=0.74+0.012-0.008=0.74

 

Path co-efficient for religion on distance of migration:

Direct:-0.15

Unanalysed due to gender:

-0.08x0.74=-0.06

Unanalysed due to dependents:

-0.04x0.11=-0.004

Total=-0.15-0.06-0.004=-0.21

 

Path co-efficient for dependents on distance of migration:

Direct: 0.11

Unanalysed due to gender:

-0.07x0.74=-0.06

Unanalysed due to religion:

-0.04x-0.15=0.006

Total=0.11-0.06+0.006=0.06

 

 

 


 

Fig. 4: Calibrated Structural Equation Model

 


Structural Equation for distance of migration:

0.74-0.21+0.06+0.98=1.57

 

Path co-efficient for gender on occupation before migration:

Direct: 0.48

Unanalysed due to religion:

-0.08x-0.10=0.008

Unanalysed due to dependents:

-0.07x0.06=-0.004

Total=0.48+0.008-0.004=0.48

 

Path co-efficient for religion on occupation before migration:

Direct:-0.10

Unanalysed due to gender:

-0.08x0.48=0.04

Unanalysed due to dependents:

-0.04x0.06=-0.002

Total=-0.10-0.04-0.002=-0.14

 

Path co-efficient for dependents on occupation before migration:

Direct: 0.06

Unanalysed due to gender:

-0.07x0.48=-0.03

Unanalysed due to religion:

-0.04x-0.10=0.004

Total=0.06-0.03+0.004=0.03

 

Structural Equation for occupation before migration:

0.48-0.14+0.03+0.98=1.35

 

Path co-efficient for gender on choice of occupation:

Direct: 0.35

Unanalysed due to religion:

-0.08x0.43=-0.03

Unanalysed due to dependents:

-0.07x0.21=-0.02

Total=0.35-0.03-0.02=0.30

 

Path co-efficient for religion on choice of occupation:

Direct: 0.43

Unanalysed due to gender:

-0.08x0.35=-0.03

Unanalysed due to dependents:

-0.04x0.21=-0.008

Tota l0.43-0.03-0.008=0.39

 

 

Path co-efficient for dependents on choice of occupation:

Direct: 0.21

Unanalysed due to gender:

-0.07x0.35=-0.02

Unanalysed due to religion:

-0.04x0.43=0.017

Total=0.21-0.02+0.017=0.21

 

Structural Equation for choice of occupation

0.31+0.39+0.21+0.97=1.88

 

Path co-efficient for gender on choice of migration:Direct: 0.79

Unanalysed due to religion:

-0.08x-0.45=0.036

Unanalysed due to dependents:

-0.07x-0.04=0.003

Total=0.79+0.036+0.003=0.83

 

Path co-efficient for religion on choice of migration:

Direct:-0.45

Unanalysed due to gender:

-0.08x0.79=-0.06

Unanalysed due to dependents:

-0.04x-0.04=0.002

Total=-0.45-0.06+0.002=-0.51

 

Path co-efficient for dependents on choice of migration:

Direct:-0.04

Unanalysed due to gender:

-0.07x0.79=-0.055

Unanalysed due to religion:

-0.04x-0.45=0.018

Total=-0.04-0.055+0.018=-0.08

 

Structural Equation for choice of migration:

0.83-0.51-0.08+0.96=1.20

 

Path co-efficient for gender on willingness to stay in same occupation:

Direct:-0.03

Unanalysed due to religion:

-0.08x0.01=-0.0008

Unanalysed due to dependents:

-0.07x0.01=-0.0007

Total=-0.03-0.0008-0.0007=-0.03

 

Path co-efficient for religion on willingness to stay in same occupation:

Direct:-0.03

Unanalysed due to gender:

-0.08x-0.03=0.0024

Unanalysed due to dependents:

-0.04x0.01=-0.0004

Total=-0.03+0.0024-0.0004=-0.03

 

Path co-efficient for dependents on willingness to stay in same occupation:

Direct: 0.01

Unanalysed due to gender:

-0.07x-0.03=0.0021

Unanalysed due to religion:

-0.04x-0.03=0.0012

Total=0.01+0.0021+0.0012=0.01

 

Path co-efficient for occupation before migration on willingness to stay in same occupation:

Direct:-0.01

Spurious:

Occupation before migration-Gender-willingness to stay in same occupation:

0.48x-0.03=-0.014

Occupation before migration-Religion-willingness to stay in same occupation:

-0.10x-0.03=0.003

Occupation before migration-Dependents-willingness to stay in same occupation:

0.06x0.01=0.0006

Total:-0.01-0.014+0.003+0.0006=-0.02

Path co-efficient for distance of migration on willingness to stay in same occupation:

Direct: 0.01

Spurious:

Distance of migration-Gender-willingness to stay in same occupation:

0.74x-0.03=-0.022

Distance of migration-Religion-willingness to stay in same occupation:

-0.15x-0.03=0.005

Distance of migration-Dependents-willingness to stay in same occupation:

0.11x0.01=0.0011

Total: 0.01-0.022+0.005+0.0011=-0.06

 

Path co-efficient for choice of occupation on willingness to stay in same occupation:

Direct: 0.01

Spurious:

Choice of occupation-Gender-willingness to stay in same occupation:

0.35x-0.03=-0.01

Choice of occupation-Religion-willingness to stay in same occupation:

0.43x-0.03=-0.013

Choice of occupation-Dependents-willingness to stay in same occupation:

0.21x0.01=0.0021

Total: 0.01-0.01-0.013+0.0021=-0.01

 

Path co-efficient for choice of migration on willingness to stay in same occupation:

Direct:-0.02

Spurious:

Choice of migration-Gender-willingness to stay in same occupation:

0.79x-0.03=-0.02

Choice of migration-Religion-willingness to stay in same occupation:

-0.45x-0.03=0.013

Choice of migration-Dependents-willingness to stay in same occupation:

-0.04x0.01=-0.0004

Total:-0.02-0.02+0.013-0.0004=-0.03

 

Structural Equation for willingness to stay in same occupation:

-0.03-0.03+0.01–0.02–0.06-0.01–0.03+0.98=0.81

 

Path co-efficient for gender on income:

Direct: 0.21

Unanalysed due to religion:

-0.08x-0.01=0.0008

Unanalysed due to dependents:

-0.07x0.05=-0.004

Total=0.21+0.0008-0.004=0.21

 

Path co-efficient for religion on income:

Direct:-0.01

Unanalysed due to gender:

-0.08x0.21=-0.02

Unanalysed due to dependents:

-0.04x0.05=-0.002

Total=-0.01-0.02-0.002=-0.03

 

Path co-efficient for dependents on income:

Direct: 0.05

Unanalysed due to gender:

-0.07x0.21=-0.015

Unanalysed due to religion:

-0.04x-0.01=0.0004

Total=0.05-0.015+0.0004=0.04

 

Path co-efficient for distance of migration on income:

Direct:-0.15

Spurious:

Distance of migration-Gender-willingness to stay in same occupation-income:

0.74x-0.03x0.25=-0.001

Distance of migration-Religion-willingness to stay in same occupation-income:

-0.15x-0.03x0.25=0.001

Distance of migration-Dependents-willingness to stay in same occupation-income:

0.11x0.01x0.25=0.003

Distance of migration-Gender-income:

0.74x0.21=0.16

Distance of migration-Religion-income:

-0.15x-0.01=0.002

Distance of migration-Dependents-income:

0.11x0.05=0.006

Distance of migration-willingness to stay in same occupation-income:

0.01x0.25=0.003

Total:-0.15-0.001+0.001+0.003+0.16+0.002+0.006+0.003=0.024

 

Path co-efficient for occupation before migration on income:

Direct: 0.17

Spurious:

Occupation before migration-Gender-willingness to stay in same occupation-income:

0.48x-0.03x0.25=-0.004

Occupation before migration-Religion-willingness to stay in same occupation-income:

-0.10x-0.03x0.25=0.001

Occupation before migration-Dependents-willingness to stay in same occupation-income:

0.06x0.01x0.25=0.0002

Occupation before migration-Gender-income:

0.48x0.21=0.10

Occupation before migration-Religion-income:

-0.10x-0.01=-0.001

Occupation before migration-Dependents-income:

0.06x0.05=0.003

Occupation before migration-willingness to stay in same occupation-income:

-0.01x0.25=-0.003

Total:0.17-0.004+0.001+0.002+0.10-0.001+0.003-0.003=0.27

 

Path co-efficient for choice of occupation on income:

Direct: 0.15

Spurious:

Occupation before migration-Gender-willingness to stay in same occupation-income:

0.35x-0.03x0.25=-0.003

Occupation before migration-Religion-willingness to stay in same occupation-income:

0.43x-0.03x0.25=-0.003

Occupation before migration-Dependents-willingness to stay in same occupation-income:

0.21x0.01x0.25=0.0005

Occupation before migration-Gender-income:

0.35x0.21=0.07

Occupation before migration-Religion-income:

0.43x-0.01=-0.004

Occupation before migration-Dependents-income:

0.21x0.05=0.01

Occupation before migration-willingness to stay in same occupation-income:

0.01x0.25=0.003

Total:0.15-0.003-0.003+0.0005+0.07-0.004+0.01+0.003=0.22

 

Path co-efficient for choice of migration on income:

Direct: 0.09

Spurious:

Choice of migration-Gender-willingness to stay in same occupation-income:

0.79x-0.03x0.25=-0.006

Choice of migration-Religion-willingness to stay in same occupation-income:

-0.45x-0.03x0.25=0.003

Choice of migration-Dependents-willingness to stay in same occupation-income:

-0.04x0.01x0.25=-0.0001

Choice of migration-Gender-income:

0.79x0.21=0.17

Choice of migration-Religion-income:

-0.45x0.09=-0.04

Choice of migration-Dependents-income:

-0.04x0.05=-0.002

Choice of migration-willingness to stay in same occupation-income:

-0.02x0.25=-0.005

Total:0.09-0.006+0.003-0.0001+0.17-0.04-0.002-0.005=0.21

 

 

Path co-efficient for willingness to stay in same occupation on income:

Direct: 0.25

Spurious:

Willingness to stay in same occupation-distance of migration-gender-income:

0.01x0.74x0.21=0.002

Willingness to stay in same occupation-distance of migration-religion-income:

0.01x-0.15x-0.01=0.00002

Willingness to stay in same occupation-distance of migration-dependents-income:

0.01x0.11x0.05=0.0001

Willingness to stay in same occupation-g0.25ender-income:

-0.03x0.21=-0.006

Willingness to stay in same occupation-religion-income:

-0.03x-0.01=0.0003

Willingness to stay in same occupation-dependents-income:

0.01x0.05=0.0005

Total:0.25+0.002+0.00002+0.0001–0.006+0.0003+0.0005=0.25

 

Structural Equation for income

0.21-0.03+0.04+0.024+0.27+0.22+0.21+0.25+0.91=2.10

 

Updated Path co-efficient model for Pondy Bazaar is illustrated in Figure 2, to which path coefficients have been computed as below:

 


 

 

Fig. 5: Updated Calibrated Structural Equation Model

 


6. DISCUSSION:

There are certain interesting points to be noted from the path co-efficient analysis. The path co-efficients can be divided into four ranges; low (<1), medium (1-1.5), high (1.5-2), very high (>2):

 

Table 1: Path co-efficient for various parameters of structural equation model

Parameters

Path co-efficient

Range

Willingness to stay

0.88

Low

Choice of Migration

1.20

Medium

Occupation before migration

1.35

Medium

Distance of Migration

1.57

High

Choice of Occupation

1.88

High

Income

2.10

Very High

Source: Researchers’ work

 

First, Income has the highest path co-efficient than any other parameter. Among the parameters considered, occupation before migration, willingness to stay, choice of migration, choice of occupation and gender have a stronger positive correlation with income than any other variable.

i.          The vendors whose occupation before migrating to Chennai was vending or business were found to have higher incomes than respondents who were either in service, agriculture or unemployed.

ii.          The respondents who were earning more showed their willingness to stay in the same occupation of vending.

iii.          Respondents who chose vending as an occupation on their own without any pressure from society had higher incomes than respondents who chose the occupation under pressure from either family or society.

iv.          However the decision to migrate was largely dependent on the societal pressure. Wherever the acquaintances and relatives encouraged the respondents to migrate, they also helped in setting up vending stalls in cities and thus the migrants who were urged by acquaintances and relatives to migrate had higher incomes than those urged by family members or those who migrated on their own will.

v.          Males had higher income than females.

 

Second, structural equation assigned high path co-efficient to choice of occupationfollowed by distance of migration.

  i.     Religion seemed to have major impact on choice of occupation. Hindus seemed to be the pre-dominant religion to take up the occupation of vending.

 ii.     Males had higher preference to choose vending as an occupation than the females considering the harassment of females in this occupation by the policemen and lack of credit facilities with women to start their vending business.

iii.     The number of dependents had a bearing on the choice of occupation. The families with large number of dependents preferred to choose vending as an occupation than families who had les number of dependents.

iv.     Males prefer to travel longer distances than females and migrated from far off places not only in Tamil Nadu such as Nagarcoil and Tuticorin but from other states as well such as Gujarat and Maharashtra.

 v.     Of the three religions, Muslims were the most migratory. They came from as far as Assam and West Bengal to work as vendors in the city as compared to Hindus and Christians who preferred to come from nearby places of Chennai.

 

Third, medium path co-efficient was found for occupation before migration and choice of migration.

  i.     Males were found to have chosen vending as their preferred occupation even after migration. However, females were either studying or engaged in agriculture prior to migration.

 ii.     However the correlation was positive between the number of dependents and choice of vending as an occupation, yet it was not quite strong.

iii.     Muslims who were engaged as vendors prior to migration, chose vending as an occupation even after migrating to Chennai. This was largely due to their contacts in the city which helped them set up their vending business in Chennai. However, Hindus were either engaged in agriculture or studying before migration and had no prior experience in vending prior to migration.

 

Fourth, path co-efficient was found to be lowest for willingness to stay in the same occupation of vending.

  i.     Though Muslims and Christians showed higher willingness to stay in vending, the co-efficient were found to be negative for Hindus.

 ii.     Females showed a preference to stay in vending as compared to males who would like to move up the ladder of economic growth and shift to formal sector over a period of time.

iii.     The families having higher dependent population expressed preference to stay in vending as compared to smaller families.

iv.     What was most interesting to observe was that the respondents whose earlier occupation (before migration) was vending did not want to continue in vending and want to shift to formal sector at some point of time in future.

 v.     Respondents who migrated from far off places showed a preference to stay in vending as compared to respondents who migrated from nearby places in Tamil Nadu.

vi.     The respondents who chose vending under pressure from society or family pressure expressed their willingness to stay in the same occupation. However the respondents who chose the occupation of vending on their own will or who willingly wanted to carry on the occupation of their fathers as vendors stated their preference to move on from vending to formal sectors in future.

vii.     The vendors who decided to migrate to Chennai and engage themselves in vending of their own free will or encouraged by their family members stated higher preference to stay in the same occupation than the respondents who chose to migrate under pressure from relatives or acquaintances.

viii.      

7. CONCLUSION:

Broadly there are two theories to understand poverty and exploring the reasons for low earning of urban dwellers, Individual and Social theory. The individual theory of poverty has been found to be operating well in a Tier II city (Vijayawada) but seems to be absent in a Tier I city (Delhi). It was found in the previous research that as the scale of the city grows individual factors have less impact on earnings of the vendors because in large cities other social, cultural and political factors come into play their role (Sharma 2017, 2018). This research is an attempt to understand whether the cultural theory of poverty holds true for street vendors of a Tier I city, Chennai. The results show that the path coefficient for income is the highest among all the variables considered which implies that relationship of all chosen cultural variables to income seems to be really strong in case of Chennai. However among the parameters considered, economic factors (occupation before migration), cyclical factors (willingness to stay), and societal factors (choice of migration, choice of occupation and gender) have a stronger impact on income. Geographical factors (distance of migration) and few societal factors (religion, dependents) do not have any impact on incomes of street vendors in the city. Since the model has been run on a Tier I city, thus the results might differ significantly when it is run on a Tier II city. Further research needs to be conducted in this direction.

 

8. ACKNOWLEDGEMENT:

The author would like to acknowledge the street vendors who participated enthusiastically in the research.

 

9. REFERENCES:

1.       Bandla, V. S. 2013. To hawk or not to hawk! In ThozhilalarKoodam: A Blog on Labour Issues in Tamil Nadu. Available at: http://tnlabour.in/service-sector/1528 [Accessed April 21, 2018].

2.       Bradshaw, T. K. (2000). Complex Community Development Projects: Collaboration, Comprehensive Programs and Community Coalitions in Complex Society. Community Development Journal, Vol. 35 (2), pp. 133-145.

3.       Lewis, 0. (1968). The Culture of Poverty. New York: Random House, Inc.

4.       Lopez, A. X. 2017. Street vendors in Chennai to get ID cards soon. The Hindu. 11 October. Available at: http://www.thehindu.com/todays-paper/tp-national/tp-tamilnadu/street-vendors-in-chennai-to-get-id-cards-soon/article19836428.ece [Accessed April 25, 2018].

5.       Myrdal, G. (1957). Economic Theory and Underdeveloped Regions. London: Gerald Duckworth and Co.

6.       Raqshan, T. 2017. Street vendors in Chennai city fight for more space. DT Next. 27 May. Available at: https://www.dtnext.in/News/City/2017/05/27081054/1034804/Street-vendors-in-Chennai-city-fight-for-more-space.vpf [Accessed April 27, 2018].

7.       Sharma, S. (2017). Individual Theory of Poverty and Informal Sector: Linkages or Alternative Explanations. International Journal of Advanced Research (IJAR), Vol. 5(10), pp. 1356-1363.

8.       Sharma, S. (2018). Individual Theory of Poverty and Informal Sector: A Case Study of Street Vendors of NDMC, Delhi. International Journal of Creative Research Thoughts (IJCRT), Vol. 6 (1), pp. 300-306.

9.       Shekhar, S. (2012). Street Vending in Chennai. Issue Brief No. 7. Chennai: IFMR Research. Available at http://www.transparentchennai.com/wp-content/uploads/downloads/2013/07/Street%20vending%20in%20Chennai.pdf[Accessed April 18, 2018).

10.     Tobin, J. (1994). Poverty in Relation to Macroeconomic Trends, Cycles, Policies. In S. H.  Danzinder, G. D. Sandefur, and D. H. Weinberg (eds), Confronting Poverty: Prescriptions for Change.  Cambridge:  Harvard University Press.

 

 

 

Received on 03.05.2018        Modified on 14.06.2018

Accepted on 15.07.2018      ©AandV Publications All right reserved

Res.  J. Humanities and Social Sciences. 2018; 9(3): 557-566.

DOI: 10.5958/2321-5828.2018.00094.3