FINANCIAL INCLUSION AND POVERTY ALLEVIATION

Financial inclusion of the poor can play a role in poverty alleviation which has been the subject of academic and policy debate during recent times. UNDP Millennium Development Goals framed in 2015, obliges to eradicate extreme poverty and to reduce the population surviving below USD one dollar a day by the year 2015. Poverty alleviation is possible through income generation of households or using a method to provide source of earnings to people (Amartya Sen). Alleviation of poverty is also possible by improving access to credit, promoting savings, and enhancing livelihoods. Traditional channels to address poverty such as transferring grants, subsidies, employment generation schemes, livelihood policies are also important, but they have not been completely effective and sufficient to tackle poverty. Better and innovative institutional mechanisms that can provide the links between the poor and the formal markets can also cause reduction in poverty. This research aims to develop a framework for financial inclusion for poverty alleviation in Odisha. This includes a basic understanding of the interrelationship between degrees of poverty alleviation and financial inclusion, drawing empirical evidence from the state of Odisha. We develop a multidimensional index that build up on discrimination and arrives at inclusion, as the complement of exclusion and renders the mathematical properties of a robust index. The multi-dimensional index of financial exclusion is computed for all the 30 districts in Odisha for the year 2011. We find that capacity barriers have played a much critical role to avert inclusiveness over the period, which is correlated with the district level Human development index (HDI) for the same period. The recommendations of this study are aimed at more effective use of the inclusive financial system in place. 

Detailed and sufficient review of literature was conducted to propose a framework for the analysis. Traditional methods of sampling design that can suffice statistical analysis are the followed approach. The use of individual univariate indicators could lead to misleading interpretation of the extent of inclusion in an economy. In order to empirically explore the scenario on the ground, a robust and more comprehensive and richer multidimensional indicator of financial inclusion needs to be developed. It is essential to conduct the field testing of hypotheses not covered in the literature to understand the barriers. It is also desirable to conduct empirical analysis for policy implications. The data Is collected in two parts; the first part data for this study was collected from households via a sample survey. The respondents of the survey included households varying by occupancy class. The district of Sundargarh from the State of Odisha was selected purposively. Sundargarh district was selected for the following reasons;

• a district from western region of the state
• a districts from agricultural and industrial region
• a district that has received attention for poverty alleviation due to large tribal population
• a district that has received attention for banking and insurance penetration surrounding major Industrial Town of Rourkela and Headquarter.
  • Socio-Economic
  • Development: Industrialization
  • capacity of BFSI Network:
  • Economic growth: SDP, IIP, the employment share
  • Prices & Agriculture

The variables collected were meaningfully correlated to confirm the standard social science research norms. The following are the major variables the data were gathered (not exclusive) during the study. The variables collected were meaningfully correlated to confirm the standard social science research norms. 

These households are classified into four broad categories: Agricultural Farmers, Artisans, Self Employed vendors, etc. A total of 290 valid responses were recorded during 2014-2016 for Sundargarh districts of Odisha. The total number of Households covered in the survey is 0.1% of all households in Sundargarh district 473,293 (in 2001). they are casual workers, wage earners, rural farmers, landless laborers, self-employed and unorganized service sector micro enterprise, slum dwellers, immigrants, minorities, older citizens, women, or geographically secluded and separated clusters. Although we cannot solve the challenges of eliminating exclusion barriers, we intend to focus on the question of poor people and their number in society. This study could help know the nexus between poverty eradication and financial inclusion for households in Odisha.

Product Testing

Field Survey

Online Survey

Data Analysis

Experimental Design

Campaign Creatives

Development of Financial Tools

CSR Assessment Tools

Designing Off Site Recruitment Tools

the total number of Households covered in the survey is .1% of all households in Sundargarh district 473,293 (census 2001).

The aim is not seek funds from the network of MIT solve. the aim to reply on self driven and self developed solution which is specific to the domain and spatial indiginous location. global banks have failed to penetrate into deep mountain hinterlands to open physical bank branches and do the hand holding to illiterate villagers to own full banking accounts in their name.

target selection, channel selectin, targeting method, product scoping, test model building, sampling, RCT based test strategy , etc

it is too slow to ensure all forms of financial inclusions checking account, health insurance, life insurance, time pension accounts, personal loans, asset loans simultaneously by one single household. 

milestones on quarterly basis on data collection, data quality checking data analysis and field validation trials etc.

empower rural households by providing them the freedom to avail and possess financial instruments

SPSS MATLAB SQL AND ADVANCED EXCEL are combined to build test modesl which are developed from field data collected using online and physical contact survey responders.

sample design, spatial location coverage, product target coverage time plan of the survey and whole lot of comprehensive factors could ensure inclusivity 

we do not propose commercial licensing of the intellectual property. we would rather beiieive in advocacy and policy planners solicitation to ensure that they implement our solution in the long run.

self funded over head contribution from the employer academic university voluntary labor hours by the team members, etc.  

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